Collate condition data
Max Lindmark
27 July, 2022
Intro
In this script, I load exchange data from datras and extract CA data (i.e. biological data), which is used for the condition model. In theory I could have joined the CPUE data to the CA data and get all important covariates and variables (oxygen, depth, ices information, densities of cod and flounder). However, the CPUE data has been standardized with respect to trawl speed, gear dimension, sweep length and trawl duration. Because many haul id’s did not have this information, the CPUE data has fewer id’s. In order to not lose condition data because I don’t have haul-level CPUE of cod and flounder, I will instead repeat the data cleaning process here, fit models to cod and flounder CPUE and predict at the location of the condition data.
Load libraries
rm(list = ls())
library(tidyverse); theme_set(theme_light(base_size = 12))
library(readxl)
library(tidylog)
library(RCurl)
library(viridis)
library(RColorBrewer)
library(patchwork)
library(janitor)
library(icesDatras)
library(mapdata)
library(patchwork)
library(rgdal)
library(raster)
library(sf)
library(rgeos)
library(chron)
library(lattice)
library(ncdf4)
library(marmap)
library(rnaturalearth)
library(rnaturalearthdata)
library(mapplots)
library(geosphere)
#remotes::install_github("pbs-assess/sdmTMB")
library(sdmTMB)For maps
world <- ne_countries(scale = "medium", returnclass = "sf")
# Specify map ranges
ymin = 54; ymax = 58; xmin = 12; xmax = 22
map_data <- rnaturalearth::ne_countries(
scale = "medium",
returnclass = "sf", continent = "europe")
# Crop the polygon for plotting and efficiency:
# st_bbox(map_data) # find the rough coordinates
swe_coast <- suppressWarnings(suppressMessages(
st_crop(map_data,
c(xmin = xmin, ymin = ymin, xmax = xmax, ymax = ymax))))
# Transform our map into UTM 33 coordinates, which is the equal-area projection we fit in:
utm_zone33 <- 32633
swe_coast_proj <- sf::st_transform(swe_coast, crs = utm_zone33)
#ggplot(swe_coast_proj) + geom_sf() Read data
# Read HH data
bits_hh <- read.csv("data/DATRAS_exchange/bits_hh.csv") %>% filter(Quarter == 4)
# Read CA data
bits_ca <- read.csv("data/DATRAS_exchange/bits_ca.csv") %>% filter(Quarter == 4)Standardize catch data
Standardize ships
# Before creating a a new ID, make sure that countries and ships names use the same format
sort(unique(bits_hh$Ship))
#> [1] "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77SE" "AA36" "ESLF"
#> [11] "ESOR" "ESTM" "LTDA" "RUJB" "RUNT"
# Change back to the old Ship name standard...
# https://vocab.ices.dk/?ref=315
# https://vocab.ices.dk/?ref=315
# Assumptions:
# SOL is Solea on ICES links above, and SOL1 is the older one of the two SOLs (1 and 2)
# DAN is Dana
# sweep %>% filter(Ship == "DANS") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "67BC") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "26D4") %>% distinct(Year) # Strange that 26DF doesn't extend far back. Which ship did the Danes use? Ok, I have no Danish data that old.
# bits_hh %>% filter(Country == "DK") %>% distinct(Year)
bits_hh <- bits_hh %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
bits_ca <- bits_ca %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))Standardize countries
# Now check which country codes are used
sort(unique(bits_hh$Country))
#> [1] "DE" "DK" "EE" "LT" "LV" "PL" "RU" "SE"
# https://www.nationsonline.org/oneworld/country_code_list.htm#E
bits_hh <- bits_hh %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
bits_ca <- bits_ca %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
# Gear? Are they the same?
sort(unique(bits_hh$Gear))
#> [1] "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "LBT" "PEL" "SON" "TVL" "TVS"Create a simple ID that works across all exchange data
# Create ID column
bits_ca <- bits_ca %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
bits_hh <- bits_hh %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
# Works like a haul-id
# bits_hh %>% group_by(IDx) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)Create the same unique haul-ID in the cpue data that I have in the sweep-file
bits_hh <- bits_hh %>%
mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":"))
bits_hh %>%
distinct(haul.id, .keep_all = TRUE) %>%
group_by(Month) %>%
summarise(n = n()) %>%
ungroup() %>%
mutate((prop = n / sum(n)) * 100)
#> # A tibble: 4 × 3
#> Month n `(prop = n/sum(n)) * 100`
#> <int> <int> <dbl>
#> 1 9 15 0.230
#> 2 10 927 14.2
#> 3 11 4948 75.9
#> 4 12 633 9.70Clean DATRAS EXCHANGE data
# Select just valid, additional and no oxygen hauls
bits_hh <- bits_hh %>%
filter(HaulVal %in% c("A","N","V"))
# Add ICES rectangle
bits_hh$Rect <- mapplots::ices.rect2(lon = bits_hh$ShootLong, lat = bits_hh$ShootLat)
# Add ICES subdivisions
shape <- shapefile("data/ICES_StatRec_mapto_ICES_Areas/StatRec_map_Areas_Full_20170124.shp")
pts <- SpatialPoints(cbind(bits_hh$ShootLong, bits_hh$ShootLat),
proj4string = CRS(proj4string(shape)))
#> Warning in proj4string(shape): CRS object has comment, which is lost in output
bits_hh$sub_div <- over(pts, shape)$Area_27
# Rename subdivisions to the more common names and do some more filtering (by sub div and area)
sort(unique(bits_hh$sub_div))
#> [1] "3.a.20" "3.a.21" "3.b.23" "3.c.22" "3.d.24" "3.d.25"
#> [7] "3.d.26" "3.d.27" "3.d.28.1" "3.d.28.2" "3.d.29"
bits_hh <- bits_hh %>%
mutate(sub_div = factor(sub_div),
sub_div = fct_recode(sub_div,
"20" = "3.a.20",
"21" = "3.a.21",
"22" = "3.c.22",
"23" = "3.b.23",
"24" = "3.d.24",
"25" = "3.d.25",
"26" = "3.d.26",
"27" = "3.d.27",
"28" = "3.d.28.1",
"28" = "3.d.28.2",
"29" = "3.d.29",
"30" = "3.d.30"),
sub_div = as.character(sub_div))
#> Warning: Unknown levels in `f`: 3.d.30
# Match columns from the HH data to the HL and CA data
sort(unique(bits_hh$sub_div))
#> [1] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
sort(colnames(bits_hh))
#> [1] "BotCurDir" "BotCurSpeed" "BotSal"
#> [4] "BotTemp" "Buoyancy" "BySpecRecCode"
#> [7] "CodendMesh" "Country" "DataType"
#> [10] "DateofCalculation" "Day" "DayNight"
#> [13] "Depth" "DepthStratum" "Distance"
#> [16] "DoorSpread" "DoorSurface" "DoorType"
#> [19] "DoorWgt" "Gear" "GearEx"
#> [22] "GroundSpeed" "haul.id" "HaulDur"
#> [25] "HaulLat" "HaulLong" "HaulNo"
#> [28] "HaulVal" "HydroStNo" "IDx"
#> [31] "KiteDim" "MaxTrawlDepth" "MinTrawlDepth"
#> [34] "Month" "Netopening" "PelSampType"
#> [37] "Quarter" "RecordType" "Rect"
#> [40] "Rigging" "SecchiDepth" "Ship"
#> [43] "Ship2" "Ship3" "ShootLat"
#> [46] "ShootLong" "SpeedWater" "StatRec"
#> [49] "StdSpecRecCode" "StNo" "sub_div"
#> [52] "SurCurDir" "SurCurSpeed" "SurSal"
#> [55] "SurTemp" "Survey" "SweepLngt"
#> [58] "SwellDir" "SwellHeight" "ThClineDepth"
#> [61] "ThermoCline" "Tickler" "TidePhase"
#> [64] "TideSpeed" "TimeShot" "TowDir"
#> [67] "Turbidity" "WarpDen" "Warpdia"
#> [70] "Warplngt" "WgtGroundRope" "WindDir"
#> [73] "WindSpeed" "WingSpread" "X"
#> [76] "Year"
bits_hh_merge <- bits_hh %>%
dplyr::select(sub_div, Rect, HaulVal, StdSpecRecCode, BySpecRecCode,
IDx, ShootLat, ShootLong)
bits_ca <- left_join(bits_ca, bits_hh_merge, by = "IDx")
# Now filter the subdivisions I want from all data sets
bits_hh <- bits_hh %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_ca <- bits_ca %>% filter(sub_div %in% c(24, 25, 26, 27, 28))Now clean the CA data and go to 1 row 1 observation
bits_ca <- bits_ca %>%
filter(SpecCode %in% c("164712", "126436") & Year < 2020) %>%
mutate(Species = "Cod")
# Now I need to copy rows with NoAtLngt > 1 so that 1 row = 1 ind
# First make a small test
# nrow(bits_ca)
# test_id <- head(filter(bits_ca, CANoAtLngt == 5))$ID[1]
# filter(bits_ca, ID == test_id & CANoAtLngt == 5)
bits_ca <- bits_ca %>% map_df(., rep, .$CANoAtLngt)
# head(data.frame(filter(bits_ca, ID == test_id & CANoAtLngt == 5)), 20)
# nrow(bits_ca)
# Looks ok!
# Standardize length and drop NA weights (need that for condition)
bits_ca <- bits_ca %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_g = IndWgt) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) # Standardize length (https://vocab.ices.dk/?ref=18)
# Plot
ggplot(bits_ca, aes(IndWgt, length_cm)) +
geom_point() +
facet_wrap(~Year)
# Remove apparent outlier
bits_ca <- bits_ca %>%
mutate(keep = ifelse(Year == 2010 & IndWgt > 12500, "N", "Y")) %>%
filter(keep == "Y") %>% dplyr::select(-keep)
ggplot(bits_ca, aes(IndWgt, length_cm)) +
geom_point() +
facet_wrap(~Year)
# Rename things and select specific columns
dat <- bits_ca %>% rename("year" = "Year",
"lat" = "ShootLat",
"lon" = "ShootLong",
"quarter" = "Quarter",
"ices_rect" = "Rect") %>%
dplyr::select(weight_g, length_cm, year, lat, lon, quarter, IDx, ices_rect, sub_div)
# > nrow(dat)
# [1] 98863dat %>% drop_na(weight_g) %>% filter(year > 1992) %>% ggplot(., aes(lon, lat)) + geom_point(size = 0.1) + facet_wrap(~year)
Add in the environmental variables
Depth
# Read the tifs
west <- raster("data/depth_geo_tif/D5_2018_rgb-1.tif")
#plot(west)
east <- raster("data/depth_geo_tif/D6_2018_rgb-1.tif")
# plot(east)
dep_rast <- raster::merge(west, east)
dat$depth <- extract(dep_rast, dat[, 5:4])
# Convert to depth (instead of elevation)
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dat$depth <- (dat$depth - max(drop_na(dat)$depth)) *-1
#> drop_na: no rows removed
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Oxygen
# Downloaded from here: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_BIO_003_012
# Extract raster points: https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
# https://rpubs.com/boyerag/297592
# https://pjbartlein.github.io/REarthSysSci/netCDF.html#get-a-variable
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float o2b[longitude,latitude,time]
#> long_name: Sea_floor_Dissolved_Oxygen_Concentration
#> missing_value: NaN
#> standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
#> units: mmol m-3
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:324
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25551.5
#> latitude Size:166
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 52.991626739502
#> valid_max: 58.4915390014648
#> longitude Size:253
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 23.013614654541
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_BIO_003_012
#> title: CMEMS V4 Reanalysis: SCOBI model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#> file_quality_index: 1
#> creation_date: 2020-11-16 UTC
#> bullentin_date: 20191201
#> start_date: 2019-12-01 UTC
#> stop_date: 2019-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 52.99163 53.02496 53.05829 53.09163 53.12496 53.15829
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)
#> [1] 253 166 324
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
# We only use Quarter 4 in this analysis, so now we want to loop through each time step,
# and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
index_keep <- which(months > 9)
oxy_q4 <- oxy_array[, , index_keep]
months_keep <- months[index_keep]
years_keep <- years[index_keep]
# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq <- seq(1, dim(oxy_q4)[3], by = 3)
# Create objects that will hold data
dlist <- list()
oxy_10 <- c()
oxy_11 <- c()
oxy_12 <- c()
oxy_ave <- c()
# Loop through the vector sequence with every third value, then take the average of
# three consecutive months (i.e. q4)
for(i in loop_seq) {
oxy_10 <- oxy_q4[, , (i)]
oxy_11 <- oxy_q4[, , (i + 1)]
oxy_12 <- oxy_q4[, , (i + 2)]
oxy_ave <- (oxy_10 + oxy_11 + oxy_12) / 3
list_pos <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist[[list_pos]] <- oxy_ave
}
# Now name the lists with the year:
names(dlist) <- unique(years_keep)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have oxygen for
d_sub_oxy <- dat %>% filter(year %in% names(dlist)) %>% droplevels()
#> filter: removed 1,305 rows (1%), 97,558 rows remaining
# Create data holding object
data_list <- list()
# ... And for the oxygen raster
raster_list <- list()
# Create factor year for indexing the list in the loop
d_sub_oxy$year_f <- as.factor(d_sub_oxy$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_oxy$year_f)) {
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a year
oxy_slice <- dlist[[i]]
# Create raster for that year (i)
r <- raster(t(oxy_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
#plot(r, main = i)
# Filter the same year (i) in the cpue data and select only coordinates
d_slice <- d_sub_oxy %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp <- SpatialPoints(d_slice)
# Extract raster value (oxygen)
rasValue <- raster::extract(r, data_sp)
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df <- as.data.frame(data_sp)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice$oxy <- rasValue
# Add in which year
d_slice$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index <- as.numeric(d_slice$year)[1] - 1992
# Add each years' data in the list
data_list[[index]] <- d_slice
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map.p <- rasterToPoints(r)
# Make the points a dataframe for ggplot
df_rast <- data.frame(map.p)
# Rename y-variable and add year
df_rast <- df_rast %>% rename("oxy" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list[[index]] <- df_rast
# Make appropriate column headings
colnames(df_rast) <- c("Longitude", "Latitude", "oxy")
# Now make the map
ggplot(data = df_rast, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = oxy)) +
geom_point(data = d_slice, aes(x = lon, y = lat, fill = oxy),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-200, 400)) +
NULL
ggsave(paste("figures/supp/cpue_oxygen_rasters/", i,".png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 96,361 rows (99%), 1,197 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,731 rows (98%), 1,827 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,528 rows (98%), 2,030 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,874 rows (98%), 1,684 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,471 rows (98%), 2,087 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,757 rows (98%), 1,801 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,653 rows (98%), 1,905 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,414 rows (97%), 3,144 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,525 rows (98%), 2,033 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,631 rows (98%), 1,927 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,149 rows (97%), 3,409 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,678 rows (95%), 4,880 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,720 rows (94%), 5,838 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,896 rows (94%), 5,662 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 90,570 rows (93%), 6,988 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,253 rows (94%), 6,305 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,495 rows (96%), 4,063 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,789 rows (95%), 4,769 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,475 rows (96%), 4,083 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,686 rows (97%), 2,872 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,286 rows (97%), 3,272 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,349 rows (96%), 4,209 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,298 rows (96%), 4,260 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,379 rows (94%), 6,179 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,746 rows (95%), 4,812 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,270 rows (97%), 3,288 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,524 rows (97%), 3,034 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_oxy <- dplyr::bind_rows(data_list)
big_raster_dat_oxy <- dplyr::bind_rows(raster_list)
# Plot data, looks like there's big inter-annual variation but a negative trend over time
big_raster_dat_oxy %>%
group_by(year) %>%
drop_na(oxy) %>%
summarise(mean_oxy = mean(oxy)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, mean_oxy)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> summarise: now 27 rows and 2 columns, ungrouped
#> mutate: new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
big_raster_dat_oxy %>%
group_by(year) %>%
drop_na(oxy) %>%
mutate(dead = ifelse(oxy < 0, "Y", "N")) %>%
filter(dead == "Y") %>%
mutate(n = n(),
year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, n)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> mutate (grouped): new variable 'dead' (character) with 2 unique values and 0% NA
#> filter (grouped): removed 437,238 rows (93%), 31,671 rows remaining
#> mutate (grouped): new variable 'n' (integer) with 27 unique values and 0% NA
#> new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new oxygen column in the original data:
str(d_sub_oxy)
#> tibble [97,558 × 11] (S3: tbl_df/tbl/data.frame)
#> $ weight_g : num [1:97558] 11 120 1416 25 733 ...
#> $ length_cm: num [1:97558] 10 22 51 14 41 41 42 43 43 43 ...
#> $ year : int [1:97558] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:97558] 54.7 54.7 54.7 54.5 54.5 ...
#> $ lon : num [1:97558] 13.1 13.1 13.1 14.3 14.3 ...
#> $ quarter : int [1:97558] 4 4 4 4 4 4 4 4 4 4 ...
#> $ IDx : chr [1:97558] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" ...
#> $ ices_rect: chr [1:97558] "38G3" "38G3" "38G3" "38G4" ...
#> $ sub_div : chr [1:97558] "24" "24" "24" "24" ...
#> $ depth : num [1:97558] 8 8 8 7 7 7 7 7 7 7 ...
#> $ year_f : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_oxy)
#> tibble [97,558 × 4] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:97558] 13.1 13.1 13.1 14.3 14.3 ...
#> $ lat : num [1:97558] 54.7 54.7 54.7 54.5 54.5 ...
#> $ oxy : num [1:97558] 324 324 324 349 349 ...
#> $ year: chr [1:97558] "1993" "1993" "1993" "1993" ...
# Create an ID for matching the oxygen data with the cpue data
dat$id_oxy <- paste(dat$year, dat$lon, dat$lat, sep = "_")
big_dat_oxy$id_oxy <- paste(big_dat_oxy$year, big_dat_oxy$lon, big_dat_oxy$lat, sep = "_")
# Which id's are not in the cpue data (dat)?
ids <- dat$id_oxy[!dat$id_oxy %in% c(big_dat_oxy$id_oxy)]
unique(ids)
#> [1] "1991_13.8_54.5167" "1991_13.45_54.75" "1991_13.5667_54.7167"
#> [4] "1991_13.6667_54.6833" "1991_13.8_54.65" "1991_13.85_54.7833"
#> [7] "1991_13.7_54.75" "1991_13.95_54.9333" "1991_13.6833_54.9833"
#> [10] "1991_13.1167_54.95" "1991_13.2435_54.9697" "1991_14.15_54.5167"
#> [13] "1991_14.0333_54.85" "1991_14.1167_54.6667" "1991_14.0833_54.5833"
#> [16] "1991_14.25_54.6" "1991_13.4833_55.0167" "1991_13.6_55.0333"
#> [19] "1991_13.85_55.1" "1991_13.9042_55.0183" "1991_13.2772_55.21"
#> [22] "1991_13.6492_55.2135" "1991_15.6596_55.4523" "1991_15.3273_55.9365"
#> [25] "1991_15.3685_55.95" "1991_15.9797_55.8632" "1991_16.0537_55.7407"
#> [28] "1991_16.431_55.5713" "1991_16.4383_55.6667" "1991_17.668_55.8398"
#> [31] "1991_17.0363_55.9378" "1991_17.7227_56.0398" "1991_18.4345_56.224"
#> [34] "1991_18.7987_57.1543" "1991_18.9297_57.112" "1991_18.8237_57.0395"
#> [37] "1991_19.1567_57.3528" "1991_14.4605_55.4568" "1991_14.2167_55.15"
#> [40] "1991_14.2833_55.1833" "1991_14.2833_55" "1991_14.488_55.6708"
#> [43] "1991_14.7052_55.574" "1991_17.5455_57.496" "1991_18.1203_57.8185"
#> [46] "1991_19.406_57.8703" "1991_19.5915_57.8896" "1991_17.0417_57.5"
#> [49] "1992_13.6333_55" "1992_14.1667_55.1333" "1992_14.2833_55.1833"
#> [52] "1992_13.1167_54.7167" "1992_13.3333_54.7" "1992_13.8_54.5167"
#> [55] "1992_13.55_54.7333" "1992_13.9_54.5" "1992_13.65_54.6833"
#> [58] "1992_13.8_54.6333" "1992_14.3_55.0333" "1992_14.35_55.1"
#> [61] "1992_13.8167_54.75" "1992_13.7_54.75" "1992_13.2_54.8667"
#> [64] "1992_13.1_54.9167" "1992_13.1167_54.9833" "1992_13.1167_54.65"
#> [67] "1992_13.8833_54.5667" "1992_13.9667_54.9333" "1992_14.2167_54.6333"
#> [70] "1992_14.3667_54.5167" "1992_14.2_54.5" "1992_14.0333_54.8667"
#> [73] "1992_14.1_54.7" "1992_14.1333_54.5667" "1992_13.45_55.0167"
#> [76] "1992_13.7667_55.1" "1992_13.55_55.0333" "1992_13.8667_55.1"
# Select only the columns we want to merge
big_dat_sub_oxy <- big_dat_oxy %>% dplyr::select(id_oxy, oxy)
# Remove duplicate ID (one oxy value per id)
big_dat_sub_oxy2 <- big_dat_sub_oxy %>% distinct(id_oxy, .keep_all = TRUE)
#> distinct: removed 94,317 rows (97%), 3,241 rows remaining
# big_dat_sub_oxy %>% group_by(id_oxy) %>% mutate(n = n()) %>% arrange(desc(n))
# Join the data with raster-derived oxygen with the full cpue data
dat <- left_join(dat, big_dat_sub_oxy2, by = "id_oxy")
#> left_join: added one column (oxy)
#> > rows only in x 1,305
#> > rows only in y ( 0)
#> > matched rows 97,558
#> > ========
#> > rows total 98,863
# Now the unit of oxygen is mmol/m3. I want it to be ml/L. The original model is in unit ml/L
# and it's been converted by the data host. Since it was converted without accounting for
# pressure or temperature, I can simply use the following conversion factor:
# 1 ml/l = 103/22.391 = 44.661 μmol/l -> 1 ml/l = 0.044661 mmol/l = 44.661 mmol/m^3 -> 0.0223909 ml/l = 1mmol/m^3
# https://ocean.ices.dk/tools/unitconversion.aspx
dat$oxy <- dat$oxy * 0.0223909
# Drop NA oxygen
dat <- dat %>% drop_na(oxy)
#> drop_na: removed 1,419 rows (1%), 97,444 rows remainingTemperature
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float bottomT[longitude,latitude,time]
#> standard_name: sea_water_potential_temperature_at_sea_floor
#> units: degrees_C
#> long_name: Sea floor potential temperature
#> missing_value: NaN
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:324
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25551.5
#> latitude Size:523
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 48.49169921875
#> valid_max: 65.8914184570312
#> longitude Size:383
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 30.2357654571533
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_PHY_003_011
#> title: CMEMS V4 Reanalysis: NEMO model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#> file_quality_index: 1
#> creation_date: 2020-11-16 UTC
#> bullentin_date: 20191201
#> start_date: 2019-12-01 UTC
#> stop_date: 2019-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 48.49170 48.52503 48.55836 48.59170 48.62503 48.65836
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get temperature
dname <- "bottomT"
temp_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(temp_array)
#> [1] 383 523 324
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
temp_array[temp_array == fillvalue$value] <- NA
# We only use Quarter 4 in this analysis, so now we want to loop through each time step,
# and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
index_keep <- which(months > 9)
# Quarter 4 by keeping months in index_keep
temp_q4 <- temp_array[, , index_keep]
months_keep <- months[index_keep]
years_keep <- years[index_keep]
# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq <- seq(1, dim(temp_q4)[3], by = 3)
# Create objects that will hold data
dlist <- list()
temp_10 <- c()
temp_11 <- c()
temp_12 <- c()
temp_ave <- c()
# Loop through the vector sequence with every third value, then take the average of
# three consecutive months (i.e. q4)
for(i in loop_seq) {
temp_10 <- temp_q4[, , (i)]
temp_11 <- temp_q4[, , (i + 1)]
temp_12 <- temp_q4[, , (i + 2)]
temp_ave <- (temp_10 + temp_11 + temp_12) / 3
list_pos <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist[[list_pos]] <- temp_ave
}
# Now name the lists with the year:
names(dlist) <- unique(years_keep)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have temperature for
d_sub_temp <- dat %>% filter(year %in% names(dlist)) %>% droplevels()
#> filter: no rows removed
# Create data holding object
data_list <- list()
# ... And for the temperature raster
raster_list <- list()
# Create factor year for indexing the list in the loop
d_sub_temp$year_f <- as.factor(d_sub_temp$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_temp$year_f)) {
# Subset a year
temp_slice <- dlist[[i]]
# Create raster for that year (i)
r <- raster(t(temp_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
#plot(r, main = i)
# Filter the same year (i) in the cpue data and select only coordinates
d_slice <- d_sub_temp %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp <- SpatialPoints(d_slice)
# Extract raster value (temperature)
rasValue <- raster::extract(r, data_sp)
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df <- as.data.frame(data_sp)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice$temp <- rasValue
# Add in which year
d_slice$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index <- as.numeric(d_slice$year)[1] - 1992
# Add each years' data in the list
data_list[[index]] <- d_slice
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map.p <- rasterToPoints(r)
# Make the points a dataframe for ggplot
df_rast <- data.frame(map.p)
# Rename y-variable and add year
df_rast <- df_rast %>% rename("temp" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list[[index]] <- df_rast
# Make appropriate column headings
colnames(df_rast) <- c("Longitude", "Latitude", "temp")
# Now make the map
ggplot(data = df_rast, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = temp)) +
geom_point(data = d_slice, aes(x = lon, y = lat, fill = temp),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(min(dat$lon), max(dat$lon)),
ylim = c(min(dat$lat), max(dat$lat))) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 17)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 17)) +
NULL
ggsave(paste("figures/supp/cpue_temp_rasters/", i,".png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 96,260 rows (99%), 1,184 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,617 rows (98%), 1,827 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,414 rows (98%), 2,030 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,760 rows (98%), 1,684 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,357 rows (98%), 2,087 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,643 rows (98%), 1,801 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,539 rows (98%), 1,905 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,300 rows (97%), 3,144 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,411 rows (98%), 2,033 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,517 rows (98%), 1,927 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,035 rows (97%), 3,409 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,577 rows (95%), 4,867 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,606 rows (94%), 5,838 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,782 rows (94%), 5,662 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 90,456 rows (93%), 6,988 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,153 rows (94%), 6,291 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,444 rows (96%), 4,000 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,677 rows (95%), 4,767 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,361 rows (96%), 4,083 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,573 rows (97%), 2,871 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,172 rows (97%), 3,272 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,243 rows (96%), 4,201 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,184 rows (96%), 4,260 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,265 rows (94%), 6,179 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,632 rows (95%), 4,812 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,156 rows (97%), 3,288 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,410 rows (97%), 3,034 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_temp <- dplyr::bind_rows(data_list)
big_raster_dat_temp <- dplyr::bind_rows(raster_list)
big_dat_temp %>% drop_na(temp) %>% summarise(max = max(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#> max
#> <dbl>
#> 1 14.5
big_dat_temp %>% drop_na(temp) %>% summarise(min = min(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#> min
#> <dbl>
#> 1 3.53
# Plot data, looks like there's big inter-annual variation but a positive trend
big_raster_dat_temp %>%
group_by(year) %>%
drop_na(temp) %>%
summarise(mean_temp = mean(temp)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, mean_temp)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> summarise: now 27 rows and 2 columns, ungrouped
#> mutate: new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new temperature column in the original data:
str(d_sub_temp)
#> tibble [97,444 × 13] (S3: tbl_df/tbl/data.frame)
#> $ weight_g : num [1:97444] 11 120 1416 25 733 ...
#> $ length_cm: num [1:97444] 10 22 51 14 41 41 42 43 43 43 ...
#> $ year : int [1:97444] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:97444] 54.7 54.7 54.7 54.5 54.5 ...
#> $ lon : num [1:97444] 13.1 13.1 13.1 14.3 14.3 ...
#> $ quarter : int [1:97444] 4 4 4 4 4 4 4 4 4 4 ...
#> $ IDx : chr [1:97444] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" ...
#> $ ices_rect: chr [1:97444] "38G3" "38G3" "38G3" "38G4" ...
#> $ sub_div : chr [1:97444] "24" "24" "24" "24" ...
#> $ depth : num [1:97444] 8 8 8 7 7 7 7 7 7 7 ...
#> $ id_oxy : chr [1:97444] "1993_13.05_54.6667" "1993_13.05_54.6667" "1993_13.05_54.6667" "1993_14.3167_54.5167" ...
#> $ oxy : num [1:97444] 7.25 7.25 7.25 7.82 7.82 ...
#> $ year_f : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_temp)
#> tibble [97,444 × 4] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:97444] 13.1 13.1 13.1 14.3 14.3 ...
#> $ lat : num [1:97444] 54.7 54.7 54.7 54.5 54.5 ...
#> $ temp: num [1:97444] 7.72 7.72 7.72 7.43 7.43 ...
#> $ year: chr [1:97444] "1993" "1993" "1993" "1993" ...
# Create an ID for matching the temperature data with the cpue data
dat$id_temp <- paste(dat$year, dat$lon, dat$lat, sep = "_")
big_dat_temp$id_temp <- paste(big_dat_temp$year, big_dat_temp$lon, big_dat_temp$lat, sep = "_")
# Which id's are not in the cpue data (dat)? (It's because I don't have those years, not about the location)
ids <- dat$id_temp[!dat$id_temp %in% c(big_dat_temp$id_temp)]
unique(ids)
#> character(0)
# Select only the columns we want to merge
big_dat_sub_temp <- big_dat_temp %>% dplyr::select(id_temp, temp)
# Remove duplicate ID (one temp value per id)
big_dat_sub_temp2 <- big_dat_sub_temp %>% distinct(id_temp, .keep_all = TRUE)
#> distinct: removed 94,211 rows (97%), 3,233 rows remaining
# Join the data with raster-derived oxygen with the full cpue data
dat <- left_join(dat, big_dat_sub_temp2, by = "id_temp")
#> left_join: added one column (temp)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 97,444
#> > ========
#> > rows total 97,444
colnames(dat)
#> [1] "weight_g" "length_cm" "year" "lat" "lon" "quarter"
#> [7] "IDx" "ices_rect" "sub_div" "depth" "id_oxy" "oxy"
#> [13] "id_temp" "temp"
dat <- dat %>% dplyr::select(-id_temp, -id_oxy)
# Drop NA temp
dat <- dat %>% drop_na(temp)
#> drop_na: no rows removedAdd UTM coords
# First add UTM coords
# Add UTM coords
# Function
LongLatToUTM <- function(x, y, zone){
xy <- data.frame(ID = 1:length(x), X = x, Y = y)
coordinates(xy) <- c("X", "Y")
proj4string(xy) <- CRS("+proj=longlat +datum=WGS84") ## for example
res <- spTransform(xy, CRS(paste("+proj=utm +zone=",zone," ellps=WGS84",sep='')))
return(as.data.frame(res))
}
utm_coords <- LongLatToUTM(dat$lon, dat$lat, zone = 33)
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition
dat$X <- utm_coords$X/1000 # for computational reasons
dat$Y <- utm_coords$Y/1000 # for computational reasonsAdd cod and flounder density covariates
# This is so that we can standardize the prediction grid with respect to the data
density <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cpue_q_1_4.csv")
density <- density %>% filter(quarter == 4)
# Load models
mcod <- readRDS("output/mcod.rds")
mfle <- readRDS("output/mfle.rds")
# We need to scale data with respect to the mean and sd in the data to it was fitted to (cpue data)
density_mean_depth <- mean(density$depth)
density_sd_depth <- sd(density$depth)
# Scale depth in the data
dat <- dat %>%
mutate(depth_sc = (depth - density_mean_depth) / density_sd_depth)
hist((density$depth - mean(density$depth)) / sd(density$depth))
hist(dat$depth_sc)
# Predict from the density models
cpue_cod <- exp(predict(mcod, newdata = dat)$est)
cpue_fle <- exp(predict(mfle, newdata = dat)$est)
dat$density_cod <- cpue_cod
dat$density_fle <- cpue_fle
# Inspect
ggplot(dat, aes(log(density_cod))) + geom_histogram()
ggplot(dat, aes(log(density_fle))) + geom_histogram()
# Remove the scaled depth variable
dat <- dat %>% dplyr::select(-depth_sc)… And secondly by doing a left_join from the CPUE data using haul.id
d_cod_select <- density %>% dplyr::select(density, IDx) %>% rename("density_cod_data" = "density")
d_fle_select <- density %>% dplyr::select(density_fle, IDx) %>% rename("density_fle_data" = "density_fle")
dat <- left_join(dat, d_cod_select)
dat <- left_join(dat, d_fle_select)
# Check how well they correspond
ggplot(dat, aes(density_cod_data, density_cod)) + geom_point() + geom_abline(color = "red")
#> Warning: Removed 10669 rows containing missing values (geom_point).
ggplot(dat, aes(density_fle_data, density_fle)) + geom_point() + geom_abline(color = "red")
#> Warning: Removed 10669 rows containing missing values (geom_point).
Add Saduria from raster covariates
saduria <- raster("data/saduria_tif/FWBiomassm_raster_19812019presHighweightcor_no0_newZi.tif")
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
saduria_longlat = projectRaster(saduria, crs = ('+proj=longlat'))
crs(saduria)
#> CRS arguments:
#> +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80
#> +units=m +no_defs
extent(saduria)
#> class : Extent
#> xmin : 4285000
#> xmax : 5440000
#> ymin : 3400000
#> ymax : 4810000
plot(saduria_longlat)
# Now extract the values from the saduria raster to the data
dat$density_saduria <- extract(saduria_longlat, dat[, 5:4])
ggplot(dat, aes(lon, lat, color = density_saduria)) +
geom_point()
Add large scale variables from the prediciton grid
# First explore what the difference is between filtering depths and oxygen within their niche rather than the total in the rectangle
pred_grid1 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/pred_grid_(1_2).csv")
#> Rows: 129346 Columns: 29
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): ices_rect
#> dbl (28): X, Y, depth, year, oxy, temp, lon, lat, sub_div, density_saduria, ...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pred_grid2 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/pred_grid_(2_2).csv")
#> Rows: 120107 Columns: 29
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): ices_rect
#> dbl (28): X, Y, depth, year, oxy, temp, lon, lat, sub_div, density_saduria, ...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pred_grid <- bind_rows(pred_grid1, pred_grid2)
# First create ID's for joining
dat <- dat %>% mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
year_sd_id = paste(year, sub_div, sep = "_"))
#> mutate: new variable 'year_rect_id' (character) with 880 unique values and 0% NA
#> new variable 'year_sd_id' (character) with 131 unique values and 0% NA
# t <- pred_grid %>%
# mutate(test_id = paste(year, ices_rect)) %>%
# group_by(test_id) %>%
# mutate(n = unique(temp_rec)) %>%
# ungroup() %>%
# distinct(n)
#
# tt <- pred_grid %>% filter(year == "1994" & ices_rect == "39G7")
# unique(tt$temp_rec)
# Ok, some weird rounding error! Anyhow...
# Take distinct year + ices_rect id's and select the variables
pred_grid_rec_vars <- pred_grid %>%
mutate(year_rect_id = paste(year, ices_rect, sep = "_")) %>%
distinct(year_rect_id, .keep_all = TRUE) %>%
dplyr::select(year_rect_id,
density_saduria_rec, density_cod_rec, density_fle_rec,
depth_rec, oxy_rec, temp_rec, biomass_spr, biomass_her)
#> mutate: new variable 'year_rect_id' (character) with 1,647 unique values and 0% NA
#> distinct: removed 247,806 rows (99%), 1,647 rows remaining
# Join!
dat <- left_join(dat, pred_grid_rec_vars)
#> Joining, by = "year_rect_id"
#> left_join: added 8 columns (density_saduria_rec, density_cod_rec, density_fle_rec, depth_rec, oxy_rec, …)
#>
#> > rows only in x 320
#>
#> > rows only in y ( 781)
#>
#> > matched rows 97,124
#>
#> > ========
#>
#> > rows total 97,444
# Take distinct year + sub_div id's and select the variables
pred_grid_sd_vars <- pred_grid %>%
mutate(year_sd_id = paste(year, sub_div, sep = "_")) %>%
distinct(year_sd_id, .keep_all = TRUE) %>%
dplyr::select(year_sd_id,
density_saduria_sd, density_cod_sd, density_fle_sd,
depth_sd, oxy_sd, temp_sd, biomass_spr_sd, biomass_her_sd)
#> mutate: new variable 'year_sd_id' (character) with 135 unique values and 0% NA
#> distinct: removed 249,318 rows (>99%), 135 rows remaining
# Join!
dat <- left_join(dat, pred_grid_sd_vars)
#> Joining, by = "year_sd_id"
#> left_join: added 8 columns (density_saduria_sd, density_cod_sd, density_fle_sd, depth_sd, oxy_sd, …)
#>
#> > rows only in x 0
#>
#> > rows only in y ( 4)
#>
#> > matched rows 97,444
#>
#> > ========
#>
#> > rows total 97,444Save data
colnames(dat)
#> [1] "weight_g" "length_cm" "year"
#> [4] "lat" "lon" "quarter"
#> [7] "IDx" "ices_rect" "sub_div"
#> [10] "depth" "oxy" "temp"
#> [13] "X" "Y" "density_cod"
#> [16] "density_fle" "density_cod_data" "density_fle_data"
#> [19] "density_saduria" "year_rect_id" "year_sd_id"
#> [22] "density_saduria_rec" "density_cod_rec" "density_fle_rec"
#> [25] "depth_rec" "oxy_rec" "temp_rec"
#> [28] "biomass_spr" "biomass_her" "density_saduria_sd"
#> [31] "density_cod_sd" "density_fle_sd" "depth_sd"
#> [34] "oxy_sd" "temp_sd" "biomass_spr_sd"
#> [37] "biomass_her_sd"
# Check for outliers
dat <- dat %>%
mutate(Fulton_K = weight_g/(0.01*length_cm^3)) %>%
filter(Fulton_K < 3 & Fulton_K > 0.15) # Visual exploration, larger values likely data entry errors
# Trim data a little bit to not make it unnecessarily large
dat <- dat %>%
dplyr::select(-IDx, -quarter, -IDx, -year_sd_id, -year_rect_id, -Fulton_K)
dat_93_06 <- dat %>% filter(year < 2007)
dat_07_19 <- dat %>% filter(year > 2006)
write.csv(dat_93_06, file = "data/for_analysis/mdat_cond_(1_2).csv", row.names = FALSE)
write.csv(dat_07_19, file = "data/for_analysis/mdat_cond_(2_2).csv", row.names = FALSE)niche_rect <- pred_grid %>%
filter(depth < 100 & oxy > 1.5) %>%
group_by(ices_rect, year) %>%
summarise(median_depth = median(depth),
median_oxy = median(oxy)) %>%
mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
type = "niche_data") %>%
ungroup()
#> filter: removed 61,897 rows (25%), 187,556 rows remaining
#> group_by: 2 grouping variables (ices_rect, year)
#> summarise: now 1,620 rows and 4 columns, one group variable remaining (ices_rect)
#> mutate (grouped): new variable 'year_rect_id' (character) with 1,620 unique values and 0% NA
#> new variable 'type' (character) with one unique value and 0% NA
#> ungroup: no grouping variables
all_rect <- pred_grid %>%
group_by(ices_rect, year) %>%
summarise(median_depth = median(depth),
median_oxy = median(oxy)) %>%
mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
type = "all_data") %>%
ungroup()
#> group_by: 2 grouping variables (ices_rect, year)
#> summarise: now 1,647 rows and 4 columns, one group variable remaining (ices_rect)
#> mutate (grouped): new variable 'year_rect_id' (character) with 1,647 unique values and 0% NA
#> new variable 'type' (character) with one unique value and 0% NA
#> ungroup: no grouping variables
all_dat <- bind_rows(all_rect, niche_rect)
# Now add in the condition data to calculate sample size per rectangle
# Filter
cond1 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cond_(1_2).csv")
#> Rows: 39391 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): ices_rect
#> dbl (24): weight_g, length_cm, year, lat, lon, sub_div, depth, oxy, temp, X,...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
cond2 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cond_(2_2).csv")
#> Rows: 58020 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): ices_rect
#> dbl (24): weight_g, length_cm, year, lat, lon, sub_div, depth, oxy, temp, X,...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
cond <- bind_rows(cond1, cond2) %>% mutate(year_rect_id = paste(year, ices_rect, sep = "_"))
#> mutate: new variable 'year_rect_id' (character) with 880 unique values and 0% NA
# Join data for lm
all_rect2 <- all_rect %>% dplyr::select(year_rect_id, median_depth) %>%
mutate(median_depth_sc = (median_depth - mean(median_depth))/sd(median_depth))
#> mutate: new variable 'median_depth_sc' (double) with 60 unique values and 0% NA
niche_rect2 <- niche_rect %>% dplyr::select(year_rect_id, median_depth) %>%
mutate(median_depth_sc = (median_depth - mean(median_depth))/sd(median_depth))
#> mutate: new variable 'median_depth_sc' (double) with 295 unique values and 0% NA
cond_all <- left_join(cond, all_rect2)
#> Joining, by = "year_rect_id"
#> left_join: added 2 columns (median_depth, median_depth_sc)
#>
#> > rows only in x 320
#>
#> > rows only in y ( 781)
#>
#> > matched rows 97,091
#>
#> > ========
#>
#> > rows total 97,411
cond_niche <- left_join(cond, niche_rect2)
#> Joining, by = "year_rect_id"
#> left_join: added 2 columns (median_depth, median_depth_sc)
#>
#> > rows only in x 320
#>
#> > rows only in y ( 754)
#>
#> > matched rows 97,091
#>
#> > ========
#>
#> > rows total 97,411
summary(lm(log(weight_g) ~ log(length_cm) + median_depth_sc, data = cond_all))
#>
#> Call:
#> lm(formula = log(weight_g) ~ log(length_cm) + median_depth_sc,
#> data = cond_all)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.56291 -0.07639 0.00456 0.08272 1.15374
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -4.6403346 0.0034142 -1359.15 <2e-16 ***
#> log(length_cm) 2.9833335 0.0009767 3054.44 <2e-16 ***
#> median_depth_sc -0.0352010 0.0006678 -52.71 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.1368 on 97088 degrees of freedom
#> (320 observations deleted due to missingness)
#> Multiple R-squared: 0.9897, Adjusted R-squared: 0.9897
#> F-statistic: 4.673e+06 on 2 and 97088 DF, p-value: < 2.2e-16
summary(lm(log(weight_g) ~ log(length_cm) + median_depth_sc, data = cond_niche))
#>
#> Call:
#> lm(formula = log(weight_g) ~ log(length_cm) + median_depth_sc,
#> data = cond_niche)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.56612 -0.07526 0.00525 0.08195 1.18544
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -4.6423652 0.0033843 -1371.75 <2e-16 ***
#> log(length_cm) 2.9866612 0.0009716 3074.12 <2e-16 ***
#> median_depth_sc -0.0340829 0.0005098 -66.86 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.1357 on 97088 degrees of freedom
#> (320 observations deleted due to missingness)
#> Multiple R-squared: 0.9899, Adjusted R-squared: 0.9899
#> F-statistic: 4.753e+06 on 2 and 97088 DF, p-value: < 2.2e-16
t <- cond_all %>% filter(median_depth < 100)
#> filter: removed 2,904 rows (3%), 94,507 rows remaining
plot(cond_all$median_depth_sc)
plot(cond_niche$median_depth_sc)
cond2 <- cond %>%
group_by(year, ices_rect) %>%
summarise(n = n()) %>%
mutate(year_rect_id = paste(year, ices_rect, sep = "_")) %>%
ungroup() %>%
dplyr::select(-year, -ices_rect)
#> group_by: 2 grouping variables (year, ices_rect)
#> summarise: now 880 rows and 3 columns, one group variable remaining (year)
#> mutate (grouped): new variable 'year_rect_id' (character) with 880 unique values and 0% NA
#> ungroup: no grouping variables
cond2 %>% arrange(n) %>% as.data.frame()
#> n year_rect_id
#> 1 1 1993_39G5
#> 2 1 1994_40G8
#> 3 1 1994_44G8
#> 4 1 1997_40G8
#> 5 1 1999_43G9
#> 6 1 2002_37G4
#> 7 1 2003_41G6
#> 8 1 2003_42G6
#> 9 1 2003_43G6
#> 10 1 2004_43G8
#> 11 1 2004_43H0
#> 12 1 2006_39G2
#> 13 1 2007_45G9
#> 14 1 2008_37G3
#> 15 1 2009_43H1
#> 16 1 2009_44H0
#> 17 1 2010_37G6
#> 18 1 2011_40G8
#> 19 1 2011_43G6
#> 20 1 2012_37G9
#> 21 1 2012_45G9
#> 22 1 2014_37G6
#> 23 1 2014_37G8
#> 24 1 2014_39G9
#> 25 1 2016_42H0
#> 26 1 2017_40G8
#> 27 1 2017_42G6
#> 28 1 2018_38G7
#> 29 1 2018_40G7
#> 30 1 2018_45H1
#> 31 1 2019_43G7
#> 32 2 1994_41G7
#> 33 2 1996_39G5
#> 34 2 1997_42G7
#> 35 2 1999_42G7
#> 36 2 2001_45G9
#> 37 2 2004_40G9
#> 38 2 2005_37G3
#> 39 2 2008_38G6
#> 40 2 2008_43G9
#> 41 2 2010_41G8
#> 42 2 2014_41G6
#> 43 2 2015_37G8
#> 44 2 2016_42G7
#> 45 2 2017_37G6
#> 46 2 2018_37G4
#> 47 2 2019_44G9
#> 48 3 2005_37G4
#> 49 3 2005_38G7
#> 50 3 2005_43G9
#> 51 3 2010_37G5
#> 52 3 2012_43G6
#> 53 3 2015_39G9
#> 54 3 2015_44H0
#> 55 3 2017_38G7
#> 56 3 2018_38G9
#> 57 3 2019_42G6
#> 58 4 1993_37G4
#> 59 4 1995_38G4
#> 60 4 1998_42G7
#> 61 4 1998_44H1
#> 62 4 2000_38G6
#> 63 4 2001_42G7
#> 64 4 2002_43G6
#> 65 4 2003_45G9
#> 66 4 2006_44G9
#> 67 4 2009_37G4
#> 68 4 2012_41H0
#> 69 4 2013_38G2
#> 70 4 2014_39G2
#> 71 4 2018_37G6
#> 72 5 1997_37G4
#> 73 5 2003_37G6
#> 74 5 2007_45H1
#> 75 5 2009_42G7
#> 76 5 2011_39G8
#> 77 5 2012_37G6
#> 78 5 2012_38G7
#> 79 5 2014_43G8
#> 80 5 2017_41H0
#> 81 6 1999_44G9
#> 82 6 2002_37G5
#> 83 6 2002_38G6
#> 84 6 2005_41G9
#> 85 6 2007_42G7
#> 86 6 2011_39G2
#> 87 6 2011_42G7
#> 88 6 2014_37G2
#> 89 6 2014_42G7
#> 90 6 2015_37G9
#> 91 6 2018_41H0
#> 92 7 1997_43G6
#> 93 7 2003_38G8
#> 94 7 2004_37G3
#> 95 7 2005_43G6
#> 96 7 2007_37G4
#> 97 7 2008_37G4
#> 98 7 2011_39G5
#> 99 7 2011_43G7
#> 100 7 2013_37G6
#> 101 7 2016_43G8
#> 102 8 1993_43G9
#> 103 8 1997_39G6
#> 104 8 1998_41G7
#> 105 8 1998_43H1
#> 106 8 2009_39G5
#> 107 8 2012_43G8
#> 108 8 2014_38G7
#> 109 8 2015_38G9
#> 110 8 2015_39G5
#> 111 8 2016_43H0
#> 112 8 2017_39G6
#> 113 8 2019_44H0
#> 114 8 2019_44H1
#> 115 9 2000_43G6
#> 116 9 2002_37G2
#> 117 9 2003_41G9
#> 118 9 2003_43G9
#> 119 9 2003_43H0
#> 120 9 2011_43G8
#> 121 9 2012_42G7
#> 122 9 2017_38G2
#> 123 10 1997_43G9
#> 124 10 1997_44G9
#> 125 10 1999_43G6
#> 126 10 2009_40H0
#> 127 10 2009_43G6
#> 128 10 2010_44H0
#> 129 10 2017_43G8
#> 130 11 1994_37G3
#> 131 11 1997_41G8
#> 132 11 2000_38G2
#> 133 11 2000_45H1
#> 134 11 2002_43H1
#> 135 11 2003_37G5
#> 136 11 2005_37G5
#> 137 11 2012_42H0
#> 138 11 2013_43G9
#> 139 11 2018_42G6
#> 140 11 2019_43G6
#> 141 12 1993_41G7
#> 142 12 1995_40G6
#> 143 12 2000_40G9
#> 144 12 2004_39G9
#> 145 12 2005_41G6
#> 146 12 2007_37G8
#> 147 12 2008_38G7
#> 148 12 2013_37G2
#> 149 12 2015_43G6
#> 150 12 2017_37G9
#> 151 12 2019_42G7
#> 152 13 2000_44G9
#> 153 13 2006_38G8
#> 154 13 2009_38G7
#> 155 13 2010_43G6
#> 156 13 2013_43G8
#> 157 13 2015_37G2
#> 158 14 2000_39G6
#> 159 14 2000_43G9
#> 160 14 2006_37G2
#> 161 14 2006_37G4
#> 162 14 2013_39G2
#> 163 14 2016_43G7
#> 164 15 2011_37G2
#> 165 15 2012_37G3
#> 166 15 2012_43H0
#> 167 15 2015_44G9
#> 168 15 2016_37G2
#> 169 15 2017_43G7
#> 170 15 2018_39G9
#> 171 15 2019_37G4
#> 172 15 2019_39G8
#> 173 16 2004_39G4
#> 174 16 2009_39G8
#> 175 16 2016_38G7
#> 176 17 1995_44G9
#> 177 17 2001_43H1
#> 178 17 2004_37G4
#> 179 17 2005_44H1
#> 180 17 2009_43G9
#> 181 17 2011_45G9
#> 182 17 2016_44G9
#> 183 18 1993_44G9
#> 184 18 1994_44G9
#> 185 18 1998_43G7
#> 186 18 1998_44G9
#> 187 18 1999_43H0
#> 188 18 2003_43G8
#> 189 18 2006_43G6
#> 190 18 2009_37G6
#> 191 18 2011_37G6
#> 192 18 2011_38G7
#> 193 18 2011_43G9
#> 194 18 2017_43G9
#> 195 18 2018_43G9
#> 196 18 2019_40G8
#> 197 19 1998_38G4
#> 198 19 2004_38G9
#> 199 19 2007_41G8
#> 200 19 2016_37G9
#> 201 20 2003_38G5
#> 202 20 2008_39G2
#> 203 20 2015_44H1
#> 204 20 2018_42G7
#> 205 20 2019_41G8
#> 206 21 2007_43G7
#> 207 21 2013_40G8
#> 208 21 2016_39G8
#> 209 22 2002_40G6
#> 210 22 2002_43H0
#> 211 22 2005_41G7
#> 212 22 2007_42G6
#> 213 22 2015_38G7
#> 214 23 2000_44H1
#> 215 23 2003_37G9
#> 216 23 2005_45G9
#> 217 23 2010_38G8
#> 218 23 2011_41G8
#> 219 23 2011_42G6
#> 220 23 2014_37G4
#> 221 23 2016_37G3
#> 222 23 2016_39G7
#> 223 23 2017_37G2
#> 224 23 2019_38G7
#> 225 24 2000_41H0
#> 226 24 2002_38G7
#> 227 24 2010_37G2
#> 228 24 2011_44G9
#> 229 24 2015_38G2
#> 230 24 2018_41G7
#> 231 25 1999_39G5
#> 232 25 2001_42G6
#> 233 25 2004_40G7
#> 234 25 2011_41G9
#> 235 25 2019_37G2
#> 236 26 2003_43H1
#> 237 26 2008_37G9
#> 238 26 2012_39G4
#> 239 26 2013_38G7
#> 240 26 2017_44G9
#> 241 26 2018_37G3
#> 242 26 2018_43G8
#> 243 26 2019_39G2
#> 244 27 2005_43G7
#> 245 27 2008_43H0
#> 246 27 2013_45H1
#> 247 28 2002_38G3
#> 248 28 2007_41G6
#> 249 28 2008_42G6
#> 250 28 2011_37G4
#> 251 28 2012_37G4
#> 252 28 2013_43H0
#> 253 28 2014_37G3
#> 254 29 2003_39G4
#> 255 29 2003_39G8
#> 256 29 2009_40G8
#> 257 29 2010_43G8
#> 258 29 2012_41G9
#> 259 29 2019_40G7
#> 260 30 1997_40G6
#> 261 30 2003_39G5
#> 262 30 2007_39G8
#> 263 30 2007_44H1
#> 264 30 2014_43G7
#> 265 30 2016_37G5
#> 266 31 2003_38G6
#> 267 31 2018_37G5
#> 268 31 2018_39G8
#> 269 32 1994_43G9
#> 270 32 2005_44G9
#> 271 32 2010_39G3
#> 272 32 2010_43G7
#> 273 32 2010_45H1
#> 274 32 2013_39G5
#> 275 32 2013_39G8
#> 276 32 2016_41H0
#> 277 33 1994_43G8
#> 278 33 2003_40G6
#> 279 33 2007_43G9
#> 280 33 2009_44G9
#> 281 33 2014_44G9
#> 282 33 2017_44H1
#> 283 34 1999_41H0
#> 284 34 2002_39G5
#> 285 34 2007_37G5
#> 286 34 2009_43G8
#> 287 34 2010_42G6
#> 288 34 2013_38G9
#> 289 34 2013_41G7
#> 290 34 2016_41G9
#> 291 34 2016_44H1
#> 292 34 2018_38G8
#> 293 34 2019_41G7
#> 294 35 2012_38G9
#> 295 35 2016_38G2
#> 296 35 2016_39G2
#> 297 35 2019_39G7
#> 298 36 2011_37G3
#> 299 36 2019_37G5
#> 300 37 2012_37G2
#> 301 37 2013_42H0
#> 302 37 2014_38G9
#> 303 37 2015_45H1
#> 304 37 2017_39G7
#> 305 37 2019_38G9
#> 306 38 1993_40G7
#> 307 38 1996_38G4
#> 308 38 2010_38G2
#> 309 39 2010_39G6
#> 310 39 2019_37G3
#> 311 40 1999_40G4
#> 312 40 2000_40G6
#> 313 40 2002_42G6
#> 314 40 2005_38G3
#> 315 40 2009_37G3
#> 316 40 2012_39G5
#> 317 41 2003_39G3
#> 318 41 2019_37G6
#> 319 42 2000_39G2
#> 320 42 2004_38G7
#> 321 43 2001_39G8
#> 322 43 2008_37G6
#> 323 43 2011_37G9
#> 324 43 2012_41G8
#> 325 43 2013_37G3
#> 326 43 2015_37G4
#> 327 43 2018_39G2
#> 328 43 2019_43G8
#> 329 44 1993_43G8
#> 330 44 1996_41G7
#> 331 44 2006_38G3
#> 332 44 2007_38G3
#> 333 44 2008_38G3
#> 334 44 2013_37G5
#> 335 44 2016_41G6
#> 336 44 2018_43H1
#> 337 45 2008_37G2
#> 338 45 2008_43G7
#> 339 45 2011_40G7
#> 340 45 2011_43H0
#> 341 45 2014_42G6
#> 342 45 2017_37G5
#> 343 46 1993_40G6
#> 344 46 1997_44H1
#> 345 46 2004_37G5
#> 346 46 2016_42G6
#> 347 46 2017_38G6
#> 348 47 1996_41G8
#> 349 47 1999_43H1
#> 350 47 2015_39G2
#> 351 48 1997_38G4
#> 352 48 1997_43G7
#> 353 48 2005_43H1
#> 354 48 2011_39G7
#> 355 48 2012_40G7
#> 356 49 1993_40G4
#> 357 49 2002_42G7
#> 358 49 2004_44G9
#> 359 49 2008_38G2
#> 360 49 2008_41H0
#> 361 49 2010_40G6
#> 362 49 2010_41H0
#> 363 49 2010_44G9
#> 364 49 2015_41G9
#> 365 49 2018_43H0
#> 366 49 2019_43H0
#> 367 50 1995_40G8
#> 368 50 2002_39G2
#> 369 50 2007_38G6
#> 370 50 2009_37G2
#> 371 50 2016_38G6
#> 372 51 1999_38G2
#> 373 51 2007_37G9
#> 374 51 2010_40G7
#> 375 51 2012_38G5
#> 376 51 2016_37G4
#> 377 52 2002_38G4
#> 378 52 2005_37G9
#> 379 52 2009_39G6
#> 380 52 2012_44G9
#> 381 52 2014_41G7
#> 382 52 2019_37G9
#> 383 53 2007_38G5
#> 384 53 2010_43H1
#> 385 53 2012_39G8
#> 386 53 2014_38G2
#> 387 54 2018_43G7
#> 388 55 2005_39G5
#> 389 55 2010_41G9
#> 390 56 1999_40G6
#> 391 56 2007_39G2
#> 392 56 2010_37G3
#> 393 56 2018_44G9
#> 394 57 2005_39G6
#> 395 58 2008_40G6
#> 396 58 2009_38G6
#> 397 58 2010_43G9
#> 398 58 2014_39G3
#> 399 59 2006_39G8
#> 400 60 2001_44G9
#> 401 60 2009_37G9
#> 402 60 2018_39G7
#> 403 61 2005_38G4
#> 404 61 2013_38G8
#> 405 61 2015_43G8
#> 406 62 1994_40G5
#> 407 62 2001_39G3
#> 408 62 2003_37G2
#> 409 62 2003_40G9
#> 410 63 1996_40G4
#> 411 63 1997_43H0
#> 412 63 1998_40G7
#> 413 63 2000_39G7
#> 414 63 2001_43H0
#> 415 63 2011_38G2
#> 416 64 1994_43G7
#> 417 64 1997_41G7
#> 418 64 2000_41G7
#> 419 64 2009_42G6
#> 420 64 2013_39G7
#> 421 65 2008_43G6
#> 422 66 1996_43G8
#> 423 66 1999_42H0
#> 424 66 2000_43G7
#> 425 66 2011_43H1
#> 426 66 2017_39G3
#> 427 66 2017_39G8
#> 428 67 2001_40G8
#> 429 67 2005_39G8
#> 430 67 2009_43G7
#> 431 67 2009_45G9
#> 432 67 2017_37G4
#> 433 67 2017_39G2
#> 434 68 1998_43G8
#> 435 68 2012_38G6
#> 436 68 2014_38G6
#> 437 68 2015_38G6
#> 438 68 2018_37G9
#> 439 68 2018_38G6
#> 440 69 2000_40G4
#> 441 69 2005_38G6
#> 442 69 2019_38G6
#> 443 70 2006_37G9
#> 444 70 2011_44H1
#> 445 70 2013_37G4
#> 446 70 2016_39G6
#> 447 70 2018_44H1
#> 448 71 2006_37G3
#> 449 71 2015_39G6
#> 450 72 2000_43H0
#> 451 73 1994_40G4
#> 452 73 2001_43G7
#> 453 73 2012_39G6
#> 454 73 2013_37G9
#> 455 74 2001_40G7
#> 456 74 2018_39G6
#> 457 74 2019_38G8
#> 458 75 1997_43H1
#> 459 75 2001_40G6
#> 460 75 2001_41G7
#> 461 75 2002_39G7
#> 462 75 2004_39H0
#> 463 75 2014_42H0
#> 464 75 2017_41G8
#> 465 76 1993_43G7
#> 466 76 2004_38G6
#> 467 76 2013_40G7
#> 468 76 2015_37G3
#> 469 77 2002_41G7
#> 470 77 2008_39G8
#> 471 77 2010_44H1
#> 472 77 2019_39G3
#> 473 78 1997_43G8
#> 474 78 2003_39G6
#> 475 78 2009_40G6
#> 476 79 2000_42G7
#> 477 80 2004_39G5
#> 478 80 2007_43G6
#> 479 80 2014_37G5
#> 480 80 2017_43H0
#> 481 81 1998_43H0
#> 482 81 2006_38G2
#> 483 81 2006_42G6
#> 484 81 2016_40G8
#> 485 82 1996_40G6
#> 486 82 2000_38G5
#> 487 82 2012_37G5
#> 488 82 2012_38G8
#> 489 82 2014_43H1
#> 490 82 2015_41G7
#> 491 83 2001_38G2
#> 492 83 2010_39G5
#> 493 84 1997_42H0
#> 494 84 2014_44H1
#> 495 85 2001_43G6
#> 496 85 2004_38G8
#> 497 85 2017_41G9
#> 498 86 1995_41G7
#> 499 86 2009_41G8
#> 500 86 2016_40G7
#> 501 86 2017_39G5
#> 502 87 2004_39G3
#> 503 87 2015_43H1
#> 504 87 2019_39G5
#> 505 87 2019_43H1
#> 506 88 1999_40G7
#> 507 89 2004_41G7
#> 508 90 2008_38G5
#> 509 90 2009_38G5
#> 510 90 2010_38G6
#> 511 90 2010_38G7
#> 512 91 1996_43G7
#> 513 91 1999_43G8
#> 514 91 2015_42H0
#> 515 92 1999_41G7
#> 516 92 2013_38G6
#> 517 92 2017_45H1
#> 518 93 2001_39G4
#> 519 93 2005_40G8
#> 520 94 2018_38G2
#> 521 95 2017_40G7
#> 522 96 2004_41G8
#> 523 96 2015_39G3
#> 524 97 2011_39G3
#> 525 97 2013_39G6
#> 526 98 2005_40G9
#> 527 98 2006_43H1
#> 528 98 2014_41G9
#> 529 99 2011_38G6
#> 530 99 2018_41G9
#> 531 100 1993_40G5
#> 532 100 2004_40G4
#> 533 100 2008_40G8
#> 534 100 2012_44H1
#> 535 101 2011_39G6
#> 536 101 2012_41G7
#> 537 102 2005_37G2
#> 538 103 2009_38G8
#> 539 103 2010_37G9
#> 540 103 2015_39G7
#> 541 104 1995_43G9
#> 542 104 1996_40G5
#> 543 105 2002_39G4
#> 544 105 2009_41G7
#> 545 105 2010_42H0
#> 546 105 2011_38G9
#> 547 105 2012_39G7
#> 548 105 2017_42H0
#> 549 106 2018_39G4
#> 550 107 2004_37G9
#> 551 107 2014_43H0
#> 552 107 2017_41G7
#> 553 108 1999_39G3
#> 554 108 2006_39G5
#> 555 108 2007_39G5
#> 556 109 2003_40G7
#> 557 109 2008_40G5
#> 558 110 2002_42H0
#> 559 110 2011_37G5
#> 560 110 2018_40G5
#> 561 111 2015_37G5
#> 562 112 2000_43G8
#> 563 113 2011_41G7
#> 564 113 2012_38G2
#> 565 113 2018_42H0
#> 566 114 1998_41G8
#> 567 114 2010_39G7
#> 568 114 2015_39H0
#> 569 114 2019_40G5
#> 570 115 2014_38G8
#> 571 116 1993_39G4
#> 572 116 2005_38G2
#> 573 116 2010_43H0
#> 574 116 2013_44G9
#> 575 117 1997_40G4
#> 576 117 2007_38G7
#> 577 118 2017_38G8
#> 578 119 2009_38G2
#> 579 119 2011_38G5
#> 580 120 1995_39G4
#> 581 120 2003_41G7
#> 582 120 2006_41G9
#> 583 120 2008_41G8
#> 584 120 2013_39G4
#> 585 121 1994_41G8
#> 586 121 2004_40G5
#> 587 123 1996_40G9
#> 588 123 2002_43G7
#> 589 123 2006_38G6
#> 590 124 1999_38G4
#> 591 124 2017_40G4
#> 592 125 2007_38G4
#> 593 125 2009_41G6
#> 594 125 2019_42H0
#> 595 126 2006_43G8
#> 596 126 2011_40G4
#> 597 127 2006_40G5
#> 598 128 2014_39G4
#> 599 129 1997_40G9
#> 600 129 2003_39G7
#> 601 129 2008_42H0
#> 602 129 2011_40G6
#> 603 129 2016_38G8
#> 604 130 2001_40G4
#> 605 130 2015_41G8
#> 606 131 2007_43G8
#> 607 131 2011_39G9
#> 608 133 2008_41G6
#> 609 133 2010_39G4
#> 610 134 2013_41G8
#> 611 135 1995_43G8
#> 612 136 2003_41G8
#> 613 136 2019_39G6
#> 614 137 2012_40G4
#> 615 138 2000_41G9
#> 616 138 2008_40G9
#> 617 140 1996_40G7
#> 618 140 2007_38G2
#> 619 140 2018_39G5
#> 620 141 1993_38G4
#> 621 141 1998_40G4
#> 622 141 2006_40G8
#> 623 141 2019_40H0
#> 624 142 1994_39G3
#> 625 143 2012_43H1
#> 626 143 2013_39G3
#> 627 145 2013_38G5
#> 628 145 2019_39H0
#> 629 146 2008_41G9
#> 630 146 2013_38G4
#> 631 146 2015_40G6
#> 632 147 2002_40G7
#> 633 148 2000_38G4
#> 634 149 2014_38G5
#> 635 151 1994_40G7
#> 636 151 2014_40G7
#> 637 152 2017_40G9
#> 638 153 1995_40G4
#> 639 153 2005_38G5
#> 640 153 2008_43G8
#> 641 153 2009_39G4
#> 642 154 1995_40G7
#> 643 154 1996_39G3
#> 644 154 2018_40G6
#> 645 154 2019_38G2
#> 646 155 2006_41G6
#> 647 155 2008_44H1
#> 648 156 2016_39G3
#> 649 158 2000_42H0
#> 650 159 2006_38G5
#> 651 160 2010_40G9
#> 652 160 2019_40G6
#> 653 161 2011_38G8
#> 654 162 1994_39G4
#> 655 162 1997_39G4
#> 656 162 2008_37G5
#> 657 162 2014_41G8
#> 658 162 2015_38G8
#> 659 163 2007_41G7
#> 660 163 2013_44H1
#> 661 164 2014_39G6
#> 662 165 1995_41G8
#> 663 165 2016_41G8
#> 664 166 2003_38G4
#> 665 166 2011_40G5
#> 666 167 1997_40G7
#> 667 167 2006_42H0
#> 668 167 2012_39G3
#> 669 168 2008_39G6
#> 670 168 2017_38G9
#> 671 170 2005_38G8
#> 672 170 2008_41G7
#> 673 171 2006_39G6
#> 674 171 2015_40G7
#> 675 171 2019_38G4
#> 676 172 2003_42H0
#> 677 172 2005_39G7
#> 678 173 2009_38G4
#> 679 173 2009_40G7
#> 680 173 2013_43H1
#> 681 174 2003_38G2
#> 682 174 2003_38G9
#> 683 174 2008_38G8
#> 684 174 2013_40G4
#> 685 175 2001_38G4
#> 686 176 2006_41G7
#> 687 178 1994_38G4
#> 688 179 2008_38G9
#> 689 179 2008_39G7
#> 690 180 1998_40G6
#> 691 180 2015_40G4
#> 692 182 2009_37G5
#> 693 182 2009_39G7
#> 694 183 1997_39G3
#> 695 183 2016_40G9
#> 696 184 1997_40G5
#> 697 184 2000_39G4
#> 698 184 2019_40G4
#> 699 185 2006_38G4
#> 700 185 2007_40G8
#> 701 185 2007_42H0
#> 702 185 2011_41H0
#> 703 185 2013_40G5
#> 704 187 2019_39G4
#> 705 189 1998_40G5
#> 706 193 2004_38G3
#> 707 193 2006_40G7
#> 708 193 2014_40G6
#> 709 193 2015_40H0
#> 710 194 1996_39G4
#> 711 194 1999_39G4
#> 712 194 2006_40H0
#> 713 194 2015_38G4
#> 714 195 1997_41G9
#> 715 195 2000_40G5
#> 716 195 2011_39G4
#> 717 196 2012_40G6
#> 718 200 2001_40G5
#> 719 200 2003_40H0
#> 720 200 2010_38G5
#> 721 200 2010_38G9
#> 722 200 2010_39G8
#> 723 202 2010_38G4
#> 724 203 1995_43G7
#> 725 204 1998_39G4
#> 726 204 2005_40G7
#> 727 205 1999_41G8
#> 728 206 2009_40G4
#> 729 207 1993_39G3
#> 730 207 2002_39G3
#> 731 207 2012_38G4
#> 732 208 2000_39G3
#> 733 208 2004_40G8
#> 734 208 2007_39G6
#> 735 210 1995_40G5
#> 736 210 2003_40G4
#> 737 210 2018_38G3
#> 738 211 2006_39G9
#> 739 211 2017_39G4
#> 740 213 2002_40G5
#> 741 213 2007_38G8
#> 742 213 2012_40H0
#> 743 214 2015_38G5
#> 744 214 2017_43H1
#> 745 215 1999_40G5
#> 746 216 2006_41G8
#> 747 216 2011_42H0
#> 748 217 2009_40G5
#> 749 218 2014_39G7
#> 750 220 2010_40G5
#> 751 223 2004_40G6
#> 752 224 2003_40G5
#> 753 224 2009_42H0
#> 754 226 2014_40G8
#> 755 226 2016_39G5
#> 756 227 2007_38G9
#> 757 228 2014_40G4
#> 758 229 2004_39G8
#> 759 229 2015_39G4
#> 760 229 2016_39H0
#> 761 230 2016_41G7
#> 762 232 2017_38G3
#> 763 233 2006_39G4
#> 764 236 2016_38G9
#> 765 236 2018_39G3
#> 766 238 2005_39G9
#> 767 238 2005_42H0
#> 768 239 2011_40H0
#> 769 239 2019_38G5
#> 770 240 2013_40G6
#> 771 241 2000_40G7
#> 772 241 2002_38G2
#> 773 241 2011_38G4
#> 774 242 2008_39G5
#> 775 242 2009_39G3
#> 776 243 2008_38G4
#> 777 244 2010_40G4
#> 778 246 2004_42H0
#> 779 246 2012_40G5
#> 780 247 2000_41G8
#> 781 249 2009_38G3
#> 782 249 2018_38G4
#> 783 250 2014_38G3
#> 784 250 2014_40H0
#> 785 251 1994_40G6
#> 786 252 2006_39G3
#> 787 256 2018_40G4
#> 788 257 2015_40G5
#> 789 258 2009_44H1
#> 790 259 2000_39G8
#> 791 260 2003_38G3
#> 792 260 2004_38G4
#> 793 264 2007_43H0
#> 794 264 2017_40G5
#> 795 266 2013_38G3
#> 796 268 2005_41G8
#> 797 270 2002_40G4
#> 798 272 2017_38G5
#> 799 276 2006_40G4
#> 800 276 2014_40G5
#> 801 276 2017_40G6
#> 802 280 2008_40G7
#> 803 281 1998_39G3
#> 804 286 2006_40G6
#> 805 287 2007_40G9
#> 806 294 1995_39G3
#> 807 297 2007_40G7
#> 808 298 2005_40G6
#> 809 298 2008_40G4
#> 810 299 2019_38G3
#> 811 300 2004_38G2
#> 812 300 2016_40G4
#> 813 301 2016_39G9
#> 814 302 2001_38G3
#> 815 302 2010_40H0
#> 816 304 2007_39G3
#> 817 305 2005_40G5
#> 818 307 2014_39G8
#> 819 309 2008_39G4
#> 820 310 2006_39G7
#> 821 311 2016_38G3
#> 822 313 2010_38G3
#> 823 315 2013_40H0
#> 824 316 2016_40G6
#> 825 318 2012_38G3
#> 826 323 1995_38G3
#> 827 324 1993_38G3
#> 828 325 2001_42H0
#> 829 325 2005_40G4
#> 830 326 2006_38G9
#> 831 329 2005_39G3
#> 832 334 2017_38G4
#> 833 337 2018_38G5
#> 834 344 2018_40H0
#> 835 346 2007_40G6
#> 836 347 2015_40G8
#> 837 350 2008_39G3
#> 838 354 2017_39G9
#> 839 356 2015_38G3
#> 840 360 1997_38G3
#> 841 365 2016_39G4
#> 842 366 2003_39H0
#> 843 366 2016_40H0
#> 844 367 2007_40G5
#> 845 378 2009_39H0
#> 846 382 2017_39H0
#> 847 392 2005_39G4
#> 848 394 2005_38G9
#> 849 394 2011_38G3
#> 850 394 2014_38G4
#> 851 401 1998_38G3
#> 852 404 2015_39G8
#> 853 409 2007_39G9
#> 854 413 2007_39G4
#> 855 414 2004_39G6
#> 856 416 2004_39G7
#> 857 422 2008_39G9
#> 858 426 2017_40H0
#> 859 427 2004_40H0
#> 860 429 2003_39G9
#> 861 431 1999_38G3
#> 862 435 2007_39H0
#> 863 456 2007_39G7
#> 864 463 2016_38G5
#> 865 475 2007_40H0
#> 866 478 2010_39H0
#> 867 487 2011_39H0
#> 868 525 1994_38G3
#> 869 536 1996_38G3
#> 870 542 2000_38G3
#> 871 547 2005_39H0
#> 872 548 2007_40G4
#> 873 604 2010_39G9
#> 874 612 2008_40H0
#> 875 629 2008_39H0
#> 876 639 2016_40G5
#> 877 763 2016_38G4
#> 878 778 2005_40H0
#> 879 798 2004_38G5
#> 880 836 2006_39H0
all_dat <- left_join(all_dat, cond2)
#> Joining, by = "year_rect_id"
#> left_join: added one column (n)
#>
#> > rows only in x 1,535
#>
#> > rows only in y ( 14)
#>
#> > matched rows 1,732
#>
#> > =======
#>
#> > rows total 3,267
hist(all_dat$n)
all_dat %>% arrange(n) %>% as.data.frame()
#> ices_rect year median_depth median_oxy year_rect_id type n
#> 1 37G3 2008 15.00000 7.04252318 2008_37G3 all_data 1
#> 2 37G4 2002 13.00000 7.55310374 2002_37G4 all_data 1
#> 3 37G6 2010 18.00000 7.94124371 2010_37G6 all_data 1
#> 4 37G6 2014 18.00000 7.32908176 2014_37G6 all_data 1
#> 5 37G8 2014 20.86536 6.64736446 2014_37G8 all_data 1
#> 6 37G9 2012 43.07566 7.02251739 2012_37G9 all_data 1
#> 7 38G7 2018 21.00000 7.40310822 2018_38G7 all_data 1
#> 8 39G2 2006 19.00000 6.77359451 2006_39G2 all_data 1
#> 9 39G5 1993 85.00000 1.55825941 1993_39G5 all_data 1
#> 10 39G9 2014 99.00000 3.13034042 2014_39G9 all_data 1
#> 11 40G7 2018 38.00000 7.61379529 2018_40G7 all_data 1
#> 12 40G8 1994 97.98204 3.32860756 1994_40G8 all_data 1
#> 13 40G8 1997 97.98204 2.94276873 1997_40G8 all_data 1
#> 14 40G8 2011 97.98204 1.84379946 2011_40G8 all_data 1
#> 15 40G8 2017 97.98204 2.68929285 2017_40G8 all_data 1
#> 16 41G6 2003 32.27464 7.62731978 2003_41G6 all_data 1
#> 17 42G6 2003 29.09908 7.58598890 2003_42G6 all_data 1
#> 18 42G6 2017 29.09908 7.27095227 2017_42G6 all_data 1
#> 19 42H0 2016 57.59084 7.56993608 2016_42H0 all_data 1
#> 20 43G6 2003 30.91015 7.30400321 2003_43G6 all_data 1
#> 21 43G6 2011 30.91015 4.92048295 2011_43G6 all_data 1
#> 22 43G7 2019 83.80202 3.75597524 2019_43G7 all_data 1
#> 23 43G8 2004 27.86322 7.91112818 2004_43G8 all_data 1
#> 24 43G9 1999 180.66532 0.12117647 1999_43G9 all_data 1
#> 25 43H0 2004 124.04904 2.36574769 2004_43H0 all_data 1
#> 26 43H1 2009 23.00000 7.56536304 2009_43H1 all_data 1
#> 27 44G8 1994 110.76247 2.65520274 1994_44G8 all_data 1
#> 28 44H0 2009 139.00000 -0.27921338 2009_44H0 all_data 1
#> 29 37G3 2008 15.00000 7.04252318 2008_37G3 niche_data 1
#> 30 37G4 2002 13.00000 7.55310374 2002_37G4 niche_data 1
#> 31 37G6 2010 18.00000 7.94124371 2010_37G6 niche_data 1
#> 32 37G6 2014 18.00000 7.32908176 2014_37G6 niche_data 1
#> 33 37G8 2014 20.86536 6.64736446 2014_37G8 niche_data 1
#> 34 37G9 2012 43.07566 7.02251739 2012_37G9 niche_data 1
#> 35 38G7 2018 21.00000 7.40310822 2018_38G7 niche_data 1
#> 36 39G2 2006 19.00000 6.77359451 2006_39G2 niche_data 1
#> 37 39G5 1993 79.00000 2.05975759 1993_39G5 niche_data 1
#> 38 39G9 2014 86.87663 4.43659578 2014_39G9 niche_data 1
#> 39 40G7 2018 38.00000 7.61379529 2018_40G7 niche_data 1
#> 40 40G8 1994 90.00000 4.27560823 1994_40G8 niche_data 1
#> 41 40G8 1997 90.00000 3.87455890 1997_40G8 niche_data 1
#> 42 40G8 2011 89.56757 2.90402990 2011_40G8 niche_data 1
#> 43 40G8 2017 90.00000 3.10645348 2017_40G8 niche_data 1
#> 44 41G6 2003 32.27464 7.62731978 2003_41G6 niche_data 1
#> 45 42G6 2003 27.40247 7.59361411 2003_42G6 niche_data 1
#> 46 42G6 2017 28.72478 7.28307473 2017_42G6 niche_data 1
#> 47 42H0 2016 43.82713 7.89440398 2016_42H0 niche_data 1
#> 48 43G6 2003 30.91015 7.30400321 2003_43G6 niche_data 1
#> 49 43G6 2011 30.91015 4.92048295 2011_43G6 niche_data 1
#> 50 43G7 2019 60.79264 6.10456440 2019_43G7 niche_data 1
#> 51 43G8 2004 27.22653 7.96317811 2004_43G8 niche_data 1
#> 52 43G9 1999 64.11560 5.23046770 1999_43G9 niche_data 1
#> 53 43H0 2004 52.76306 7.46517460 2004_43H0 niche_data 1
#> 54 43H1 2009 23.00000 7.56536304 2009_43H1 niche_data 1
#> 55 44G8 1994 27.80362 7.94615985 1994_44G8 niche_data 1
#> 56 44H0 2009 55.04318 6.41729513 2009_44H0 niche_data 1
#> 57 37G3 2005 15.00000 6.90368132 2005_37G3 all_data 2
#> 58 37G4 2018 13.00000 7.66608766 2018_37G4 all_data 2
#> 59 37G6 2017 18.00000 7.60525041 2017_37G6 all_data 2
#> 60 37G8 2015 20.86536 7.02239861 2015_37G8 all_data 2
#> 61 38G6 2008 26.52553 7.51053972 2008_38G6 all_data 2
#> 62 39G5 1996 85.00000 0.78096472 1996_39G5 all_data 2
#> 63 40G9 2004 83.07490 3.76914662 2004_40G9 all_data 2
#> 64 41G6 2014 32.27464 6.60575707 2014_41G6 all_data 2
#> 65 41G7 1994 38.00000 7.80753057 1994_41G7 all_data 2
#> 66 41G8 2010 96.71104 1.58156640 2010_41G8 all_data 2
#> 67 42G7 1997 79.04017 2.64258821 1997_42G7 all_data 2
#> 68 42G7 1999 79.04017 2.25255568 1999_42G7 all_data 2
#> 69 42G7 2016 79.04017 2.38158067 2016_42G7 all_data 2
#> 70 43G9 2008 180.66532 -0.74241444 2008_43G9 all_data 2
#> 71 44G9 2019 98.95766 1.83687666 2019_44G9 all_data 2
#> 72 37G3 2005 15.00000 6.90368132 2005_37G3 niche_data 2
#> 73 37G4 2018 13.00000 7.66608766 2018_37G4 niche_data 2
#> 74 37G6 2017 18.00000 7.60525041 2017_37G6 niche_data 2
#> 75 37G8 2015 20.86536 7.02239861 2015_37G8 niche_data 2
#> 76 38G6 2008 26.43109 7.51095449 2008_38G6 niche_data 2
#> 77 39G5 1996 76.00000 2.58868678 1996_39G5 niche_data 2
#> 78 40G9 2004 82.00000 3.88205815 2004_40G9 niche_data 2
#> 79 41G6 2014 32.27464 6.60575707 2014_41G6 niche_data 2
#> 80 41G7 1994 38.00000 7.80753057 1994_41G7 niche_data 2
#> 81 41G8 2010 38.75330 6.69639869 2010_41G8 niche_data 2
#> 82 42G7 1997 64.91632 4.78624119 1997_42G7 niche_data 2
#> 83 42G7 1999 65.76437 3.37135997 1999_42G7 niche_data 2
#> 84 42G7 2016 61.97936 4.53726700 2016_42G7 niche_data 2
#> 85 43G9 2008 65.81358 4.56399125 2008_43G9 niche_data 2
#> 86 44G9 2019 46.28079 6.10583140 2019_44G9 niche_data 2
#> 87 37G4 2005 13.00000 7.32508624 2005_37G4 all_data 3
#> 88 37G5 2010 20.00000 7.92823521 2010_37G5 all_data 3
#> 89 38G7 2005 21.00000 7.61807145 2005_38G7 all_data 3
#> 90 38G7 2017 21.00000 7.55112627 2017_38G7 all_data 3
#> 91 38G9 2018 91.00641 3.29989341 2018_38G9 all_data 3
#> 92 39G9 2015 99.00000 2.35879825 2015_39G9 all_data 3
#> 93 42G6 2019 29.09908 7.61255562 2019_42G6 all_data 3
#> 94 43G6 2012 30.91015 7.09055911 2012_43G6 all_data 3
#> 95 43G9 2005 180.66532 0.37801714 2005_43G9 all_data 3
#> 96 44H0 2015 139.00000 3.09611457 2015_44H0 all_data 3
#> 97 37G4 2005 13.00000 7.32508624 2005_37G4 niche_data 3
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#> 1277 38G8 2011 40.19787 6.30268575 2011_38G8 all_data 161
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#> 1566 40G4 2002 36.50172 7.70767242 2002_40G4 niche_data 270
#> 1567 38G5 2017 61.20802 3.97708334 2017_38G5 all_data 272
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#> 1576 40G7 2008 38.00000 7.53641807 2008_40G7 niche_data 280
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#> 1578 39G3 1998 34.94275 6.69857929 1998_39G3 niche_data 281
#> 1579 40G6 2006 50.04572 6.31682059 2006_40G6 all_data 286
#> 1580 40G6 2006 48.62833 6.51699351 2006_40G6 niche_data 286
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#> 1591 38G3 2019 33.62888 6.47869123 2019_38G3 all_data 299
#> 1592 38G3 2019 33.62888 6.47869123 2019_38G3 niche_data 299
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#> 1598 39G9 2016 86.00000 4.48798564 2016_39G9 niche_data 301
#> 1599 38G3 2001 33.62888 6.42969077 2001_38G3 all_data 302
#> 1600 40H0 2010 44.00000 7.48592706 2010_40H0 all_data 302
#> 1601 38G3 2001 33.62888 6.42969077 2001_38G3 niche_data 302
#> 1602 40H0 2010 44.00000 7.48592706 2010_40H0 niche_data 302
#> 1603 39G3 2007 34.94275 5.77635299 2007_39G3 all_data 304
#> 1604 39G3 2007 34.94275 5.77635299 2007_39G3 niche_data 304
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#> 1607 39G8 2014 90.00000 4.53384141 2014_39G8 all_data 307
#> 1608 39G8 2014 88.00000 4.71667328 2014_39G8 niche_data 307
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#> 1614 38G3 2016 33.62888 6.58433350 2016_38G3 niche_data 311
#> 1615 38G3 2010 33.62888 6.75243397 2010_38G3 all_data 313
#> 1616 38G3 2010 33.62888 6.75243397 2010_38G3 niche_data 313
#> 1617 40H0 2013 44.00000 7.57399354 2013_40H0 all_data 315
#> 1618 40H0 2013 44.00000 7.57399354 2013_40H0 niche_data 315
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#> 1620 40G6 2016 49.14320 6.07540381 2016_40G6 niche_data 316
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#> 1622 38G3 2012 33.62888 6.16972177 2012_38G3 niche_data 318
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#> 1634 39G3 2005 34.94275 5.53513159 2005_39G3 niche_data 329
#> 1635 38G4 2017 25.69453 7.03468798 2017_38G4 all_data 334
#> 1636 38G4 2017 25.69453 7.03468798 2017_38G4 niche_data 334
#> 1637 38G5 2018 61.20802 4.47664504 2018_38G5 all_data 337
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#> 1639 40H0 2018 44.00000 6.49050152 2018_40H0 all_data 344
#> 1640 40H0 2018 44.00000 6.49050152 2018_40H0 niche_data 344
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#> 1647 39G9 2017 99.00000 2.73148844 2017_39G9 all_data 354
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#> 1702 40H0 2007 44.00000 7.25713292 2007_40H0 niche_data 475
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#> 1704 39H0 2010 28.00000 7.71854084 2010_39H0 niche_data 478
#> 1705 39H0 2011 28.00000 7.41536649 2011_39H0 all_data 487
#> 1706 39H0 2011 28.00000 7.41536649 2011_39H0 niche_data 487
#> 1707 38G3 1994 33.62888 6.34268935 1994_38G3 all_data 525
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#> 1718 39G9 2010 86.00000 3.55012318 2010_39G9 niche_data 604
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#> 1732 39H0 2006 28.00000 7.47667114 2006_39H0 niche_data 836
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#> 1740 37G2 2000 14.28574 5.27109639 2000_37G2 all_data NA
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#> 1807 37G8 2009 20.86536 6.63211300 2009_37G8 all_data NA
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#> 1839 38G5 1999 61.20802 3.46964785 1999_38G5 all_data NA
#> 1840 38G5 2001 61.20802 4.00514716 2001_38G5 all_data NA
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#> 1849 38G6 2001 26.52553 7.53696000 2001_38G6 all_data NA
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#> 1851 38G7 1994 21.00000 7.62870196 1994_38G7 all_data NA
#> 1852 38G7 1995 21.00000 7.34043636 1995_38G7 all_data NA
#> 1853 38G7 1996 21.00000 7.72085964 1996_38G7 all_data NA
#> 1854 38G7 1997 21.00000 7.50529819 1997_38G7 all_data NA
#> 1855 38G7 1998 21.00000 7.75350536 1998_38G7 all_data NA
#> 1856 38G7 1999 21.00000 7.75870685 1999_38G7 all_data NA
#> 1857 38G7 2000 21.00000 7.48371628 2000_38G7 all_data NA
#> 1858 38G7 2001 21.00000 7.69145452 2001_38G7 all_data NA
#> 1859 38G7 2003 21.00000 7.39710867 2003_38G7 all_data NA
#> 1860 38G7 2006 21.00000 7.51662002 2006_38G7 all_data NA
#> 1861 38G8 1993 40.19787 6.73713071 1993_38G8 all_data NA
#> 1862 38G8 1994 40.19787 6.43502842 1994_38G8 all_data NA
#> 1863 38G8 1995 40.19787 6.32884280 1995_38G8 all_data NA
#> 1864 38G8 1996 40.19787 6.56708547 1996_38G8 all_data NA
#> 1865 38G8 1997 40.19787 6.27642834 1997_38G8 all_data NA
#> 1866 38G8 1998 40.19787 5.96843285 1998_38G8 all_data NA
#> 1867 38G8 1999 40.19787 6.41948188 1999_38G8 all_data NA
#> 1868 38G8 2000 40.19787 5.94023097 2000_38G8 all_data NA
#> 1869 38G8 2001 40.19787 6.88402504 2001_38G8 all_data NA
#> 1870 38G8 2002 40.19787 6.77307490 2002_38G8 all_data NA
#> 1871 38G9 1993 91.00641 4.25710348 1993_38G9 all_data NA
#> 1872 38G9 1994 91.00641 4.79218865 1994_38G9 all_data NA
#> 1873 38G9 1995 91.00641 5.10249055 1995_38G9 all_data NA
#> 1874 38G9 1996 91.00641 6.05044237 1996_38G9 all_data NA
#> 1875 38G9 1997 91.00641 2.37103035 1997_38G9 all_data NA
#> 1876 38G9 1998 91.00641 2.57706680 1998_38G9 all_data NA
#> 1877 38G9 1999 91.00641 2.46085770 1999_38G9 all_data NA
#> 1878 38G9 2000 91.00641 1.76079027 2000_38G9 all_data NA
#> 1879 38G9 2001 91.00641 3.90812899 2001_38G9 all_data NA
#> 1880 38G9 2002 91.00641 5.16704319 2002_38G9 all_data NA
#> 1881 38G9 2009 91.00641 3.75242316 2009_38G9 all_data NA
#> 1882 38H0 1993 25.40843 8.11768631 1993_38H0 all_data NA
#> 1883 38H0 1994 25.40843 7.73542899 1994_38H0 all_data NA
#> 1884 38H0 1995 25.40843 7.57679975 1995_38H0 all_data NA
#> 1885 38H0 1996 25.40843 7.50906781 1996_38H0 all_data NA
#> 1886 38H0 1997 25.40843 7.30467319 1997_38H0 all_data NA
#> 1887 38H0 1998 25.40843 7.90287733 1998_38H0 all_data NA
#> 1888 38H0 1999 25.40843 7.73133571 1999_38H0 all_data NA
#> 1889 38H0 2000 25.40843 7.48040935 2000_38H0 all_data NA
#> 1890 38H0 2001 25.40843 7.75845265 2001_38H0 all_data NA
#> 1891 38H0 2002 25.40843 7.77892181 2002_38H0 all_data NA
#> 1892 38H0 2003 25.40843 7.81739805 2003_38H0 all_data NA
#> 1893 38H0 2004 25.40843 7.68964871 2004_38H0 all_data NA
#> 1894 38H0 2005 25.40843 7.44535883 2005_38H0 all_data NA
#> 1895 38H0 2006 25.40843 6.99158635 2006_38H0 all_data NA
#> 1896 38H0 2007 25.40843 7.60890696 2007_38H0 all_data NA
#> 1897 38H0 2008 25.40843 7.63600128 2008_38H0 all_data NA
#> 1898 38H0 2009 25.40843 7.76965866 2009_38H0 all_data NA
#> 1899 38H0 2010 25.40843 8.06326072 2010_38H0 all_data NA
#> 1900 38H0 2011 25.40843 7.75620335 2011_38H0 all_data NA
#> 1901 38H0 2012 25.40843 7.90984354 2012_38H0 all_data NA
#> 1902 38H0 2013 25.40843 7.65943229 2013_38H0 all_data NA
#> 1903 38H0 2014 25.40843 7.50772141 2014_38H0 all_data NA
#> 1904 38H0 2015 25.40843 7.86688978 2015_38H0 all_data NA
#> 1905 38H0 2016 25.40843 8.25953099 2016_38H0 all_data NA
#> 1906 38H0 2017 25.40843 8.07327909 2017_38H0 all_data NA
#> 1907 38H0 2018 25.40843 7.98754044 2018_38H0 all_data NA
#> 1908 38H0 2019 25.40843 7.97582971 2019_38H0 all_data NA
#> 1909 38H1 1993 19.05070 8.07086738 1993_38H1 all_data NA
#> 1910 38H1 1994 19.05070 7.39894801 1994_38H1 all_data NA
#> 1911 38H1 1995 19.05070 7.16752888 1995_38H1 all_data NA
#> 1912 38H1 1996 19.05070 6.93238265 1996_38H1 all_data NA
#> 1913 38H1 1997 19.05070 7.28553821 1997_38H1 all_data NA
#> 1914 38H1 1998 19.05070 7.80666691 1998_38H1 all_data NA
#> 1915 38H1 1999 19.05070 7.29755746 1999_38H1 all_data NA
#> 1916 38H1 2000 19.05070 7.11444096 2000_38H1 all_data NA
#> 1917 38H1 2001 19.05070 7.46701962 2001_38H1 all_data NA
#> 1918 38H1 2002 19.05070 7.74018533 2002_38H1 all_data NA
#> 1919 38H1 2003 19.05070 7.72443087 2003_38H1 all_data NA
#> 1920 38H1 2004 19.05070 7.25328379 2004_38H1 all_data NA
#> 1921 38H1 2005 19.05070 6.97672099 2005_38H1 all_data NA
#> 1922 38H1 2006 19.05070 6.90305419 2006_38H1 all_data NA
#> 1923 38H1 2007 19.05070 7.49408782 2007_38H1 all_data NA
#> 1924 38H1 2008 19.05070 7.48430650 2008_38H1 all_data NA
#> 1925 38H1 2009 19.05070 7.72732625 2009_38H1 all_data NA
#> 1926 38H1 2010 19.05070 7.92293670 2010_38H1 all_data NA
#> 1927 38H1 2011 19.05070 7.74005428 2011_38H1 all_data NA
#> 1928 38H1 2012 19.05070 7.87143226 2012_38H1 all_data NA
#> 1929 38H1 2013 19.05070 7.78247626 2013_38H1 all_data NA
#> 1930 38H1 2014 19.05070 7.50278451 2014_38H1 all_data NA
#> 1931 38H1 2015 19.05070 7.84719548 2015_38H1 all_data NA
#> 1932 38H1 2016 19.05070 8.26214908 2016_38H1 all_data NA
#> 1933 38H1 2017 19.05070 8.06476599 2017_38H1 all_data NA
#> 1934 38H1 2018 19.05070 7.96728259 2018_38H1 all_data NA
#> 1935 38H1 2019 19.05070 8.01308126 2019_38H1 all_data NA
#> 1936 39G2 1993 19.00000 7.29217886 1993_39G2 all_data NA
#> 1937 39G2 1994 19.00000 7.08028842 1994_39G2 all_data NA
#> 1938 39G2 1995 19.00000 7.12534761 1995_39G2 all_data NA
#> 1939 39G2 1996 19.00000 7.23473904 1996_39G2 all_data NA
#> 1940 39G2 1997 19.00000 7.52556012 1997_39G2 all_data NA
#> 1941 39G2 1998 19.00000 7.44898598 1998_39G2 all_data NA
#> 1942 39G2 1999 19.00000 7.23632361 1999_39G2 all_data NA
#> 1943 39G2 2001 19.00000 7.73925850 2001_39G2 all_data NA
#> 1944 39G2 2003 19.00000 7.30094799 2003_39G2 all_data NA
#> 1945 39G2 2004 19.00000 7.31416437 2004_39G2 all_data NA
#> 1946 39G2 2005 19.00000 6.86376368 2005_39G2 all_data NA
#> 1947 39G2 2009 19.00000 7.25172088 2009_39G2 all_data NA
#> 1948 39G2 2010 19.00000 7.57748679 2010_39G2 all_data NA
#> 1949 39G2 2012 19.00000 7.53586179 2012_39G2 all_data NA
#> 1950 39G5 1994 85.00000 1.32231893 1994_39G5 all_data NA
#> 1951 39G5 1995 85.00000 0.02901467 1995_39G5 all_data NA
#> 1952 39G5 1997 85.00000 0.65457873 1997_39G5 all_data NA
#> 1953 39G5 1998 85.00000 0.21451859 1998_39G5 all_data NA
#> 1954 39G5 2000 85.00000 -0.77975655 2000_39G5 all_data NA
#> 1955 39G5 2001 85.00000 1.06989232 2001_39G5 all_data NA
#> 1956 39G5 2014 85.00000 0.76333313 2014_39G5 all_data NA
#> 1957 39G6 1993 68.00000 4.49597131 1993_39G6 all_data NA
#> 1958 39G6 1994 68.00000 2.93506846 1994_39G6 all_data NA
#> 1959 39G6 1995 68.00000 3.04636055 1995_39G6 all_data NA
#> 1960 39G6 1996 68.00000 4.49052279 1996_39G6 all_data NA
#> 1961 39G6 1998 68.00000 3.66795816 1998_39G6 all_data NA
#> 1962 39G6 1999 68.00000 3.28455310 1999_39G6 all_data NA
#> 1963 39G6 2001 68.00000 3.29019701 2001_39G6 all_data NA
#> 1964 39G6 2002 68.00000 3.29929326 2002_39G6 all_data NA
#> 1965 39G7 1993 56.11417 6.48620451 1993_39G7 all_data NA
#> 1966 39G7 1994 56.11417 6.13502179 1994_39G7 all_data NA
#> 1967 39G7 1995 56.11417 6.03689879 1995_39G7 all_data NA
#> 1968 39G7 1996 56.11417 6.69399534 1996_39G7 all_data NA
#> 1969 39G7 1997 56.11417 6.09415120 1997_39G7 all_data NA
#> 1970 39G7 1998 56.11417 5.97197878 1998_39G7 all_data NA
#> 1971 39G7 1999 56.11417 6.01249510 1999_39G7 all_data NA
#> 1972 39G7 2001 56.11417 6.31732678 2001_39G7 all_data NA
#> 1973 39G8 1993 90.00000 4.97020557 1993_39G8 all_data NA
#> 1974 39G8 1994 90.00000 3.74756687 1994_39G8 all_data NA
#> 1975 39G8 1995 90.00000 4.10354855 1995_39G8 all_data NA
#> 1976 39G8 1996 90.00000 5.10373877 1996_39G8 all_data NA
#> 1977 39G8 1997 90.00000 4.16211860 1997_39G8 all_data NA
#> 1978 39G8 1998 90.00000 3.40637995 1998_39G8 all_data NA
#> 1979 39G8 1999 90.00000 3.14816833 1999_39G8 all_data NA
#> 1980 39G8 2002 90.00000 3.94785051 2002_39G8 all_data NA
#> 1981 39G9 1993 99.00000 4.24553493 1993_39G9 all_data NA
#> 1982 39G9 1994 99.00000 3.01737739 1994_39G9 all_data NA
#> 1983 39G9 1995 99.00000 2.62102809 1995_39G9 all_data NA
#> 1984 39G9 1996 99.00000 4.39815711 1996_39G9 all_data NA
#> 1985 39G9 1997 99.00000 2.00703022 1997_39G9 all_data NA
#> 1986 39G9 1998 99.00000 1.47906916 1998_39G9 all_data NA
#> 1987 39G9 1999 99.00000 1.19346156 1999_39G9 all_data NA
#> 1988 39G9 2000 99.00000 1.36701571 2000_39G9 all_data NA
#> 1989 39G9 2001 99.00000 2.07158100 2001_39G9 all_data NA
#> 1990 39G9 2002 99.00000 2.86535853 2002_39G9 all_data NA
#> 1991 39G9 2009 99.00000 2.33038809 2009_39G9 all_data NA
#> 1992 39G9 2012 99.00000 2.76619754 2012_39G9 all_data NA
#> 1993 39G9 2013 99.00000 2.65258806 2013_39G9 all_data NA
#> 1994 39G9 2019 99.00000 3.43805253 2019_39G9 all_data NA
#> 1995 39H0 1993 28.00000 7.66373752 1993_39H0 all_data NA
#> 1996 39H0 1994 28.00000 7.53838912 1994_39H0 all_data NA
#> 1997 39H0 1995 28.00000 7.46610932 1995_39H0 all_data NA
#> 1998 39H0 1996 28.00000 7.80617631 1996_39H0 all_data NA
#> 1999 39H0 1997 28.00000 7.24553021 1997_39H0 all_data NA
#> 2000 39H0 1998 28.00000 7.61357032 1998_39H0 all_data NA
#> 2001 39H0 1999 28.00000 7.71378980 1999_39H0 all_data NA
#> 2002 39H0 2000 28.00000 7.55370793 2000_39H0 all_data NA
#> 2003 39H0 2001 28.00000 7.63957830 2001_39H0 all_data NA
#> 2004 39H0 2002 28.00000 7.69008934 2002_39H0 all_data NA
#> 2005 39H0 2012 28.00000 7.41403360 2012_39H0 all_data NA
#> 2006 39H0 2013 28.00000 7.55982960 2013_39H0 all_data NA
#> 2007 39H0 2014 28.00000 7.41652280 2014_39H0 all_data NA
#> 2008 39H0 2018 28.00000 7.16969025 2018_39H0 all_data NA
#> 2009 39H1 1993 16.94447 7.92848003 1993_39H1 all_data NA
#> 2010 39H1 1994 16.94447 6.76520130 1994_39H1 all_data NA
#> 2011 39H1 1995 16.94447 6.84790373 1995_39H1 all_data NA
#> 2012 39H1 1996 16.94447 6.30069030 1996_39H1 all_data NA
#> 2013 39H1 1997 16.94447 7.15760498 1997_39H1 all_data NA
#> 2014 39H1 1998 16.94447 7.71061296 1998_39H1 all_data NA
#> 2015 39H1 1999 16.94447 6.64860804 1999_39H1 all_data NA
#> 2016 39H1 2000 16.94447 6.88042867 2000_39H1 all_data NA
#> 2017 39H1 2001 16.94447 7.28687867 2001_39H1 all_data NA
#> 2018 39H1 2002 16.94447 7.45305159 2002_39H1 all_data NA
#> 2019 39H1 2003 16.94447 7.22602350 2003_39H1 all_data NA
#> 2020 39H1 2004 16.94447 7.09008829 2004_39H1 all_data NA
#> 2021 39H1 2005 16.94447 6.83502359 2005_39H1 all_data NA
#> 2022 39H1 2006 16.94447 6.97542183 2006_39H1 all_data NA
#> 2023 39H1 2007 16.94447 6.89413450 2007_39H1 all_data NA
#> 2024 39H1 2008 16.94447 7.13092141 2008_39H1 all_data NA
#> 2025 39H1 2009 16.94447 7.63121452 2009_39H1 all_data NA
#> 2026 39H1 2010 16.94447 7.66135109 2010_39H1 all_data NA
#> 2027 39H1 2011 16.94447 7.35640886 2011_39H1 all_data NA
#> 2028 39H1 2012 16.94447 7.51877304 2012_39H1 all_data NA
#> 2029 39H1 2013 16.94447 7.23910587 2013_39H1 all_data NA
#> 2030 39H1 2014 16.94447 7.06062064 2014_39H1 all_data NA
#> 2031 39H1 2015 16.94447 7.26020262 2015_39H1 all_data NA
#> 2032 39H1 2016 16.94447 7.85906982 2016_39H1 all_data NA
#> 2033 39H1 2017 16.94447 7.95343597 2017_39H1 all_data NA
#> 2034 39H1 2018 16.94447 7.17325372 2018_39H1 all_data NA
#> 2035 39H1 2019 16.94447 7.63963868 2019_39H1 all_data NA
#> 2036 40G8 1993 97.98204 4.72147136 1993_40G8 all_data NA
#> 2037 40G8 1996 97.98204 4.13542295 1996_40G8 all_data NA
#> 2038 40G8 1998 97.98204 2.41018887 1998_40G8 all_data NA
#> 2039 40G8 1999 97.98204 2.21819527 1999_40G8 all_data NA
#> 2040 40G8 2000 97.98204 2.57848526 2000_40G8 all_data NA
#> 2041 40G8 2002 97.98204 2.98772334 2002_40G8 all_data NA
#> 2042 40G8 2003 97.98204 3.44267116 2003_40G8 all_data NA
#> 2043 40G8 2010 97.98204 2.33200149 2010_40G8 all_data NA
#> 2044 40G8 2012 97.98204 3.19110875 2012_40G8 all_data NA
#> 2045 40G8 2018 97.98204 2.89867230 2018_40G8 all_data NA
#> 2046 40G9 1993 83.07490 5.49383162 1993_40G9 all_data NA
#> 2047 40G9 1994 83.07490 4.10045274 1994_40G9 all_data NA
#> 2048 40G9 1995 83.07490 4.44485057 1995_40G9 all_data NA
#> 2049 40G9 1998 83.07490 2.53299253 1998_40G9 all_data NA
#> 2050 40G9 1999 83.07490 2.39334762 1999_40G9 all_data NA
#> 2051 40G9 2001 83.07490 2.85973694 2001_40G9 all_data NA
#> 2052 40G9 2002 83.07490 3.36717385 2002_40G9 all_data NA
#> 2053 40G9 2006 83.07490 3.49360473 2006_40G9 all_data NA
#> 2054 40G9 2009 83.07490 2.96704744 2009_40G9 all_data NA
#> 2055 40G9 2011 83.07490 2.25333587 2011_40G9 all_data NA
#> 2056 40G9 2012 83.07490 3.21750024 2012_40G9 all_data NA
#> 2057 40G9 2013 83.07490 3.56934926 2013_40G9 all_data NA
#> 2058 40G9 2014 83.07490 3.43339813 2014_40G9 all_data NA
#> 2059 40G9 2015 83.07490 3.94684700 2015_40G9 all_data NA
#> 2060 40G9 2018 83.07490 3.20259478 2018_40G9 all_data NA
#> 2061 40G9 2019 83.07490 3.64144684 2019_40G9 all_data NA
#> 2062 40H0 1993 44.00000 7.17913771 1993_40H0 all_data NA
#> 2063 40H0 1994 44.00000 7.24683351 1994_40H0 all_data NA
#> 2064 40H0 1995 44.00000 7.22433971 1995_40H0 all_data NA
#> 2065 40H0 1996 44.00000 7.62571470 1996_40H0 all_data NA
#> 2066 40H0 1997 44.00000 7.39709618 1997_40H0 all_data NA
#> 2067 40H0 1998 44.00000 7.40917676 1998_40H0 all_data NA
#> 2068 40H0 1999 44.00000 7.56059886 1999_40H0 all_data NA
#> 2069 40H0 2000 44.00000 7.34911329 2000_40H0 all_data NA
#> 2070 40H0 2001 44.00000 7.41942531 2001_40H0 all_data NA
#> 2071 40H0 2002 44.00000 7.67278711 2002_40H0 all_data NA
#> 2072 40H1 1993 13.18982 7.44742885 1993_40H1 all_data NA
#> 2073 40H1 1994 13.18982 7.23045131 1994_40H1 all_data NA
#> 2074 40H1 1995 13.18982 7.23908378 1995_40H1 all_data NA
#> 2075 40H1 1996 13.18982 7.67636949 1996_40H1 all_data NA
#> 2076 40H1 1997 13.18982 7.18711157 1997_40H1 all_data NA
#> 2077 40H1 1998 13.18982 7.57713233 1998_40H1 all_data NA
#> 2078 40H1 1999 13.18982 7.48610539 1999_40H1 all_data NA
#> 2079 40H1 2000 13.18982 7.19185509 2000_40H1 all_data NA
#> 2080 40H1 2001 13.18982 7.37156771 2001_40H1 all_data NA
#> 2081 40H1 2002 13.18982 7.59728574 2002_40H1 all_data NA
#> 2082 40H1 2003 13.18982 7.12506958 2003_40H1 all_data NA
#> 2083 40H1 2004 13.18982 7.65046426 2004_40H1 all_data NA
#> 2084 40H1 2005 13.18982 7.27986552 2005_40H1 all_data NA
#> 2085 40H1 2006 13.18982 7.14262932 2006_40H1 all_data NA
#> 2086 40H1 2007 13.18982 7.15018764 2007_40H1 all_data NA
#> 2087 40H1 2008 13.18982 7.48599405 2008_40H1 all_data NA
#> 2088 40H1 2009 13.18982 7.46993707 2009_40H1 all_data NA
#> 2089 40H1 2010 13.18982 7.60644904 2010_40H1 all_data NA
#> 2090 40H1 2011 13.18982 7.31931898 2011_40H1 all_data NA
#> 2091 40H1 2012 13.18982 7.38152185 2012_40H1 all_data NA
#> 2092 40H1 2013 13.18982 7.44941703 2013_40H1 all_data NA
#> 2093 40H1 2014 13.18982 7.51954068 2014_40H1 all_data NA
#> 2094 40H1 2015 13.18982 7.33573727 2015_40H1 all_data NA
#> 2095 40H1 2016 13.18982 7.74778505 2016_40H1 all_data NA
#> 2096 40H1 2017 13.18982 7.47806416 2017_40H1 all_data NA
#> 2097 40H1 2018 13.18982 7.18133051 2018_40H1 all_data NA
#> 2098 40H1 2019 13.18982 7.38656169 2019_40H1 all_data NA
#> 2099 41G4 1993 29.22442 7.66056021 1993_41G4 all_data NA
#> 2100 41G4 1994 29.22442 7.45234631 1994_41G4 all_data NA
#> 2101 41G4 1995 29.22442 6.94411951 1995_41G4 all_data NA
#> 2102 41G4 1996 29.22442 7.21988025 1996_41G4 all_data NA
#> 2103 41G4 1997 29.22442 7.62431948 1997_41G4 all_data NA
#> 2104 41G4 1998 29.22442 7.70125693 1998_41G4 all_data NA
#> 2105 41G4 1999 29.22442 7.69384207 1999_41G4 all_data NA
#> 2106 41G4 2000 29.22442 6.91988984 2000_41G4 all_data NA
#> 2107 41G4 2001 29.22442 7.29063214 2001_41G4 all_data NA
#> 2108 41G4 2002 29.22442 7.58115988 2002_41G4 all_data NA
#> 2109 41G4 2003 29.22442 7.55986832 2003_41G4 all_data NA
#> 2110 41G4 2004 29.22442 7.77263941 2004_41G4 all_data NA
#> 2111 41G4 2005 29.22442 7.53867894 2005_41G4 all_data NA
#> 2112 41G4 2006 29.22442 7.10086731 2006_41G4 all_data NA
#> 2113 41G4 2007 29.22442 7.23592604 2007_41G4 all_data NA
#> 2114 41G4 2008 29.22442 7.20942611 2008_41G4 all_data NA
#> 2115 41G4 2009 29.22442 7.83967088 2009_41G4 all_data NA
#> 2116 41G4 2010 29.22442 7.57369406 2010_41G4 all_data NA
#> 2117 41G4 2011 29.22442 7.70051496 2011_41G4 all_data NA
#> 2118 41G4 2012 29.22442 7.41120771 2012_41G4 all_data NA
#> 2119 41G4 2013 29.22442 7.08645872 2013_41G4 all_data NA
#> 2120 41G4 2014 29.22442 7.31129774 2014_41G4 all_data NA
#> 2121 41G4 2015 29.22442 7.27558323 2015_41G4 all_data NA
#> 2122 41G4 2016 29.22442 7.21916562 2016_41G4 all_data NA
#> 2123 41G4 2017 29.22442 7.23886359 2017_41G4 all_data NA
#> 2124 41G4 2018 29.22442 7.75843358 2018_41G4 all_data NA
#> 2125 41G4 2019 29.22442 7.71037704 2019_41G4 all_data NA
#> 2126 41G5 1993 34.39299 7.76013927 1993_41G5 all_data NA
#> 2127 41G5 1994 34.39299 7.58057826 1994_41G5 all_data NA
#> 2128 41G5 1995 34.39299 7.19083529 1995_41G5 all_data NA
#> 2129 41G5 1996 34.39299 7.50230180 1996_41G5 all_data NA
#> 2130 41G5 1997 34.39299 7.89047207 1997_41G5 all_data NA
#> 2131 41G5 1998 34.39299 7.75781746 1998_41G5 all_data NA
#> 2132 41G5 1999 34.39299 7.89821806 1999_41G5 all_data NA
#> 2133 41G5 2000 34.39299 7.35182425 2000_41G5 all_data NA
#> 2134 41G5 2001 34.39299 7.53732334 2001_41G5 all_data NA
#> 2135 41G5 2002 34.39299 7.75094648 2002_41G5 all_data NA
#> 2136 41G5 2003 34.39299 7.74432544 2003_41G5 all_data NA
#> 2137 41G5 2004 34.39299 7.66013497 2004_41G5 all_data NA
#> 2138 41G5 2005 34.39299 8.05143985 2005_41G5 all_data NA
#> 2139 41G5 2006 34.39299 7.42255899 2006_41G5 all_data NA
#> 2140 41G5 2007 34.39299 7.46013589 2007_41G5 all_data NA
#> 2141 41G5 2008 34.39299 7.30024381 2008_41G5 all_data NA
#> 2142 41G5 2009 34.39299 7.82440918 2009_41G5 all_data NA
#> 2143 41G5 2010 34.39299 7.57815225 2010_41G5 all_data NA
#> 2144 41G5 2011 34.39299 7.07066604 2011_41G5 all_data NA
#> 2145 41G5 2012 34.39299 7.17843880 2012_41G5 all_data NA
#> 2146 41G5 2013 34.39299 7.07767122 2013_41G5 all_data NA
#> 2147 41G5 2014 34.39299 7.12154771 2014_41G5 all_data NA
#> 2148 41G5 2015 34.39299 7.14734944 2015_41G5 all_data NA
#> 2149 41G5 2016 34.39299 7.13405120 2016_41G5 all_data NA
#> 2150 41G5 2017 34.39299 7.13528823 2017_41G5 all_data NA
#> 2151 41G5 2018 34.39299 7.74903899 2018_41G5 all_data NA
#> 2152 41G5 2019 34.39299 7.41379322 2019_41G5 all_data NA
#> 2153 41G6 1993 32.27464 7.65958786 1993_41G6 all_data NA
#> 2154 41G6 1994 32.27464 7.35507010 1994_41G6 all_data NA
#> 2155 41G6 1995 32.27464 7.28047796 1995_41G6 all_data NA
#> 2156 41G6 1996 32.27464 7.22587455 1996_41G6 all_data NA
#> 2157 41G6 1997 32.27464 7.75848151 1997_41G6 all_data NA
#> 2158 41G6 1998 32.27464 7.26127803 1998_41G6 all_data NA
#> 2159 41G6 1999 32.27464 7.61267123 1999_41G6 all_data NA
#> 2160 41G6 2000 32.27464 7.30705022 2000_41G6 all_data NA
#> 2161 41G6 2001 32.27464 7.60571260 2001_41G6 all_data NA
#> 2162 41G6 2002 32.27464 7.70979607 2002_41G6 all_data NA
#> 2163 41G6 2004 32.27464 7.30891457 2004_41G6 all_data NA
#> 2164 41G6 2010 32.27464 7.10978431 2010_41G6 all_data NA
#> 2165 41G6 2011 32.27464 6.22970988 2011_41G6 all_data NA
#> 2166 41G6 2012 32.27464 7.15040203 2012_41G6 all_data NA
#> 2167 41G6 2013 32.27464 7.18952819 2013_41G6 all_data NA
#> 2168 41G6 2015 32.27464 6.89059324 2015_41G6 all_data NA
#> 2169 41G6 2017 32.27464 6.77858231 2017_41G6 all_data NA
#> 2170 41G6 2018 32.27464 7.67497859 2018_41G6 all_data NA
#> 2171 41G6 2019 32.27464 7.12551594 2019_41G6 all_data NA
#> 2172 41G7 2010 38.00000 6.23922185 2010_41G7 all_data NA
#> 2173 41G8 1993 96.71104 4.41267478 1993_41G8 all_data NA
#> 2174 41G8 2001 96.71104 1.17999848 2001_41G8 all_data NA
#> 2175 41G8 2002 96.71104 1.96729045 2002_41G8 all_data NA
#> 2176 41G8 2018 96.71104 1.33477489 2018_41G8 all_data NA
#> 2177 41G9 1993 123.90523 3.73860820 1993_41G9 all_data NA
#> 2178 41G9 1994 123.90523 2.76994686 1994_41G9 all_data NA
#> 2179 41G9 1995 123.90523 2.60538030 1995_41G9 all_data NA
#> 2180 41G9 1996 123.90523 4.18253988 1996_41G9 all_data NA
#> 2181 41G9 1998 123.90523 1.61088836 1998_41G9 all_data NA
#> 2182 41G9 1999 123.90523 1.65261641 1999_41G9 all_data NA
#> 2183 41G9 2001 123.90523 2.37515211 2001_41G9 all_data NA
#> 2184 41G9 2002 123.90523 2.98152141 2002_41G9 all_data NA
#> 2185 41G9 2004 123.90523 3.10913151 2004_41G9 all_data NA
#> 2186 41G9 2007 123.90523 1.15600250 2007_41G9 all_data NA
#> 2187 41G9 2009 123.90523 2.43754062 2009_41G9 all_data NA
#> 2188 41G9 2013 123.90523 2.08197150 2013_41G9 all_data NA
#> 2189 41G9 2019 123.90523 3.47091402 2019_41G9 all_data NA
#> 2190 41H0 1993 28.04549 7.53692190 1993_41H0 all_data NA
#> 2191 41H0 1994 28.04549 7.54643308 1994_41H0 all_data NA
#> 2192 41H0 1995 28.04549 7.49984819 1995_41H0 all_data NA
#> 2193 41H0 1996 28.04549 7.78563904 1996_41H0 all_data NA
#> 2194 41H0 1997 28.04549 7.52148318 1997_41H0 all_data NA
#> 2195 41H0 1998 28.04549 7.56308856 1998_41H0 all_data NA
#> 2196 41H0 2001 28.04549 7.74257756 2001_41H0 all_data NA
#> 2197 41H0 2002 28.04549 7.91604973 2002_41H0 all_data NA
#> 2198 41H0 2003 28.04549 7.27388825 2003_41H0 all_data NA
#> 2199 41H0 2004 28.04549 7.51453518 2004_41H0 all_data NA
#> 2200 41H0 2005 28.04549 7.54628763 2005_41H0 all_data NA
#> 2201 41H0 2006 28.04549 7.49715391 2006_41H0 all_data NA
#> 2202 41H0 2007 28.04549 7.40963896 2007_41H0 all_data NA
#> 2203 41H0 2009 28.04549 7.51369732 2009_41H0 all_data NA
#> 2204 41H0 2013 28.04549 7.74939701 2013_41H0 all_data NA
#> 2205 41H0 2014 28.04549 7.60618633 2014_41H0 all_data NA
#> 2206 41H0 2015 28.04549 7.38449543 2015_41H0 all_data NA
#> 2207 41H0 2019 28.04549 7.33867377 2019_41H0 all_data NA
#> 2208 41H1 1993 11.11676 7.67226039 1993_41H1 all_data NA
#> 2209 41H1 1994 11.11676 7.40334892 1994_41H1 all_data NA
#> 2210 41H1 1995 11.11676 7.34137785 1995_41H1 all_data NA
#> 2211 41H1 1996 11.11676 7.89110937 1996_41H1 all_data NA
#> 2212 41H1 1997 11.11676 7.29637583 1997_41H1 all_data NA
#> 2213 41H1 1998 11.11676 7.56440082 1998_41H1 all_data NA
#> 2214 41H1 1999 11.11676 7.55951397 1999_41H1 all_data NA
#> 2215 41H1 2000 11.11676 7.22091540 2000_41H1 all_data NA
#> 2216 41H1 2001 11.11676 7.49940079 2001_41H1 all_data NA
#> 2217 41H1 2002 11.11676 7.84510655 2002_41H1 all_data NA
#> 2218 41H1 2003 11.11676 7.19102042 2003_41H1 all_data NA
#> 2219 41H1 2004 11.11676 7.63260665 2004_41H1 all_data NA
#> 2220 41H1 2005 11.11676 7.25788373 2005_41H1 all_data NA
#> 2221 41H1 2006 11.11676 7.23212700 2006_41H1 all_data NA
#> 2222 41H1 2007 11.11676 7.12188686 2007_41H1 all_data NA
#> 2223 41H1 2008 11.11676 7.51304572 2008_41H1 all_data NA
#> 2224 41H1 2009 11.11676 7.43739221 2009_41H1 all_data NA
#> 2225 41H1 2010 11.11676 7.69574607 2010_41H1 all_data NA
#> 2226 41H1 2011 11.11676 7.43306156 2011_41H1 all_data NA
#> 2227 41H1 2012 11.11676 7.49419732 2012_41H1 all_data NA
#> 2228 41H1 2013 11.11676 7.55566754 2013_41H1 all_data NA
#> 2229 41H1 2014 11.11676 7.60086473 2014_41H1 all_data NA
#> 2230 41H1 2015 11.11676 7.45818187 2015_41H1 all_data NA
#> 2231 41H1 2016 11.11676 7.89068095 2016_41H1 all_data NA
#> 2232 41H1 2017 11.11676 7.63635804 2017_41H1 all_data NA
#> 2233 41H1 2018 11.11676 7.36072421 2018_41H1 all_data NA
#> 2234 41H1 2019 11.11676 7.49874547 2019_41H1 all_data NA
#> 2235 42G6 1993 29.09908 7.73389630 1993_42G6 all_data NA
#> 2236 42G6 1994 29.09908 7.42195232 1994_42G6 all_data NA
#> 2237 42G6 1995 29.09908 7.50306251 1995_42G6 all_data NA
#> 2238 42G6 1996 29.09908 7.01549591 1996_42G6 all_data NA
#> 2239 42G6 1997 29.09908 7.60542062 1997_42G6 all_data NA
#> 2240 42G6 1998 29.09908 7.61993613 1998_42G6 all_data NA
#> 2241 42G6 1999 29.09908 7.58934345 1999_42G6 all_data NA
#> 2242 42G6 2000 29.09908 7.35193473 2000_42G6 all_data NA
#> 2243 42G6 2004 29.09908 7.91552127 2004_42G6 all_data NA
#> 2244 42G6 2005 29.09908 7.87826817 2005_42G6 all_data NA
#> 2245 42G6 2012 29.09908 7.52284180 2012_42G6 all_data NA
#> 2246 42G6 2013 29.09908 7.34800517 2013_42G6 all_data NA
#> 2247 42G6 2015 29.09908 7.09332054 2015_42G6 all_data NA
#> 2248 42G7 1993 79.04017 5.30827735 1993_42G7 all_data NA
#> 2249 42G7 1994 79.04017 2.64723813 1994_42G7 all_data NA
#> 2250 42G7 1995 79.04017 3.99965039 1995_42G7 all_data NA
#> 2251 42G7 1996 79.04017 1.87000687 1996_42G7 all_data NA
#> 2252 42G7 2003 79.04017 1.29609002 2003_42G7 all_data NA
#> 2253 42G7 2004 79.04017 2.41048636 2004_42G7 all_data NA
#> 2254 42G7 2005 79.04017 3.82369308 2005_42G7 all_data NA
#> 2255 42G7 2006 79.04017 0.94196275 2006_42G7 all_data NA
#> 2256 42G7 2008 79.04017 1.85699820 2008_42G7 all_data NA
#> 2257 42G7 2010 79.04017 2.87824646 2010_42G7 all_data NA
#> 2258 42G7 2013 79.04017 0.62550888 2013_42G7 all_data NA
#> 2259 42G7 2015 79.04017 1.42330132 2015_42G7 all_data NA
#> 2260 42G7 2017 79.04017 0.90914205 2017_42G7 all_data NA
#> 2261 42G8 1993 36.38828 7.83322641 1993_42G8 all_data NA
#> 2262 42G8 1994 36.38828 8.04074584 1994_42G8 all_data NA
#> 2263 42G8 1995 36.38828 7.80998146 1995_42G8 all_data NA
#> 2264 42G8 1996 36.38828 7.62805369 1996_42G8 all_data NA
#> 2265 42G8 1997 36.38828 7.99604491 1997_42G8 all_data NA
#> 2266 42G8 1998 36.38828 7.64342145 1998_42G8 all_data NA
#> 2267 42G8 1999 36.38828 7.80214805 1999_42G8 all_data NA
#> 2268 42G8 2000 36.38828 7.49862812 2000_42G8 all_data NA
#> 2269 42G8 2001 36.38828 7.99254410 2001_42G8 all_data NA
#> 2270 42G8 2002 36.38828 7.83644464 2002_42G8 all_data NA
#> 2271 42G8 2003 36.38828 7.82224300 2003_42G8 all_data NA
#> 2272 42G8 2004 36.38828 7.67659323 2004_42G8 all_data NA
#> 2273 42G8 2005 36.38828 7.84528715 2005_42G8 all_data NA
#> 2274 42G8 2006 36.38828 7.30247150 2006_42G8 all_data NA
#> 2275 42G8 2007 36.38828 7.71226262 2007_42G8 all_data NA
#> 2276 42G8 2008 36.38828 7.45872692 2008_42G8 all_data NA
#> 2277 42G8 2009 36.38828 7.43115545 2009_42G8 all_data NA
#> 2278 42G8 2010 36.38828 7.74884506 2010_42G8 all_data NA
#> 2279 42G8 2011 36.38828 7.36274559 2011_42G8 all_data NA
#> 2280 42G8 2012 36.38828 7.26829466 2012_42G8 all_data NA
#> 2281 42G8 2013 36.38828 7.60170998 2013_42G8 all_data NA
#> 2282 42G8 2014 36.38828 7.42341723 2014_42G8 all_data NA
#> 2283 42G8 2015 36.38828 7.30951033 2015_42G8 all_data NA
#> 2284 42G8 2016 36.38828 7.47031058 2016_42G8 all_data NA
#> 2285 42G8 2017 36.38828 6.85864597 2017_42G8 all_data NA
#> 2286 42G8 2018 36.38828 7.55873606 2018_42G8 all_data NA
#> 2287 42G8 2019 36.38828 7.42588358 2019_42G8 all_data NA
#> 2288 42G9 1993 179.00230 -0.21352503 1993_42G9 all_data NA
#> 2289 42G9 1994 179.00230 0.51030758 1994_42G9 all_data NA
#> 2290 42G9 1995 179.00230 -0.34526808 1995_42G9 all_data NA
#> 2291 42G9 1996 179.00230 -0.29707984 1996_42G9 all_data NA
#> 2292 42G9 1997 179.00230 -0.38342595 1997_42G9 all_data NA
#> 2293 42G9 1998 179.00230 -0.21528312 1998_42G9 all_data NA
#> 2294 42G9 1999 179.00230 -0.69655762 1999_42G9 all_data NA
#> 2295 42G9 2000 179.00230 -2.39074087 2000_42G9 all_data NA
#> 2296 42G9 2001 179.00230 -2.22052172 2001_42G9 all_data NA
#> 2297 42G9 2002 179.00230 -2.28917644 2002_42G9 all_data NA
#> 2298 42G9 2003 179.00230 -0.61435996 2003_42G9 all_data NA
#> 2299 42G9 2004 179.00230 -0.53995784 2004_42G9 all_data NA
#> 2300 42G9 2005 179.00230 -1.23835560 2005_42G9 all_data NA
#> 2301 42G9 2006 179.00230 -0.97308776 2006_42G9 all_data NA
#> 2302 42G9 2007 179.00230 -1.46781806 2007_42G9 all_data NA
#> 2303 42G9 2008 179.00230 -1.62227181 2008_42G9 all_data NA
#> 2304 42G9 2009 179.00230 -1.88283012 2009_42G9 all_data NA
#> 2305 42G9 2010 179.00230 -1.78089556 2010_42G9 all_data NA
#> 2306 42G9 2011 179.00230 -2.43109821 2011_42G9 all_data NA
#> 2307 42G9 2012 179.00230 -2.38501055 2012_42G9 all_data NA
#> 2308 42G9 2013 179.00230 -2.10965908 2013_42G9 all_data NA
#> 2309 42G9 2014 179.00230 -0.59403330 2014_42G9 all_data NA
#> 2310 42G9 2015 179.00230 -0.49015185 2015_42G9 all_data NA
#> 2311 42G9 2016 179.00230 -0.16466085 2016_42G9 all_data NA
#> 2312 42G9 2017 179.00230 -0.49548829 2017_42G9 all_data NA
#> 2313 42G9 2018 179.00230 -1.25204475 2018_42G9 all_data NA
#> 2314 42G9 2019 179.00230 -0.56669399 2019_42G9 all_data NA
#> 2315 42H0 1993 57.59084 7.44341531 1993_42H0 all_data NA
#> 2316 42H0 1994 57.59084 7.29781040 1994_42H0 all_data NA
#> 2317 42H0 1995 57.59084 7.20050707 1995_42H0 all_data NA
#> 2318 42H0 1996 57.59084 7.76952950 1996_42H0 all_data NA
#> 2319 42H0 1998 57.59084 7.00731055 1998_42H0 all_data NA
#> 2320 42H1 1993 18.30025 7.98388131 1993_42H1 all_data NA
#> 2321 42H1 1994 18.30025 7.73413010 1994_42H1 all_data NA
#> 2322 42H1 1995 18.30025 7.53345711 1995_42H1 all_data NA
#> 2323 42H1 1996 18.30025 7.94023218 1996_42H1 all_data NA
#> 2324 42H1 1997 18.30025 7.45455444 1997_42H1 all_data NA
#> 2325 42H1 1998 18.30025 7.78536054 1998_42H1 all_data NA
#> 2326 42H1 1999 18.30025 7.65605818 1999_42H1 all_data NA
#> 2327 42H1 2000 18.30025 7.38661038 2000_42H1 all_data NA
#> 2328 42H1 2001 18.30025 7.68775677 2001_42H1 all_data NA
#> 2329 42H1 2002 18.30025 8.01096340 2002_42H1 all_data NA
#> 2330 42H1 2003 18.30025 7.37061505 2003_42H1 all_data NA
#> 2331 42H1 2004 18.30025 7.66457465 2004_42H1 all_data NA
#> 2332 42H1 2005 18.30025 7.50144000 2005_42H1 all_data NA
#> 2333 42H1 2006 18.30025 7.52218130 2006_42H1 all_data NA
#> 2334 42H1 2007 18.30025 7.60482170 2007_42H1 all_data NA
#> 2335 42H1 2008 18.30025 7.71537788 2008_42H1 all_data NA
#> 2336 42H1 2009 18.30025 7.72670913 2009_42H1 all_data NA
#> 2337 42H1 2010 18.30025 7.91550677 2010_42H1 all_data NA
#> 2338 42H1 2011 18.30025 7.61017497 2011_42H1 all_data NA
#> 2339 42H1 2012 18.30025 7.66729048 2012_42H1 all_data NA
#> 2340 42H1 2013 18.30025 7.70589821 2013_42H1 all_data NA
#> 2341 42H1 2014 18.30025 7.65363411 2014_42H1 all_data NA
#> 2342 42H1 2015 18.30025 7.54550955 2015_42H1 all_data NA
#> 2343 42H1 2016 18.30025 7.99614969 2016_42H1 all_data NA
#> 2344 42H1 2017 18.30025 7.79599645 2017_42H1 all_data NA
#> 2345 42H1 2018 18.30025 7.55210058 2018_42H1 all_data NA
#> 2346 42H1 2019 18.30025 7.56149286 2019_42H1 all_data NA
#> 2347 43G6 1993 30.91015 7.59425001 1993_43G6 all_data NA
#> 2348 43G6 1994 30.91015 6.98145891 1994_43G6 all_data NA
#> 2349 43G6 1995 30.91015 7.42949538 1995_43G6 all_data NA
#> 2350 43G6 1996 30.91015 7.09143358 1996_43G6 all_data NA
#> 2351 43G6 1998 30.91015 6.90988165 1998_43G6 all_data NA
#> 2352 43G6 2004 30.91015 7.77762803 2004_43G6 all_data NA
#> 2353 43G6 2013 30.91015 6.91941191 2013_43G6 all_data NA
#> 2354 43G6 2014 30.91015 6.70009889 2014_43G6 all_data NA
#> 2355 43G6 2016 30.91015 7.66555512 2016_43G6 all_data NA
#> 2356 43G6 2017 30.91015 6.90064678 2017_43G6 all_data NA
#> 2357 43G6 2018 30.91015 7.40607598 2018_43G6 all_data NA
#> 2358 43G7 1999 83.80202 2.58598322 1999_43G7 all_data NA
#> 2359 43G7 2003 83.80202 1.39856821 2003_43G7 all_data NA
#> 2360 43G7 2004 83.80202 2.48957133 2004_43G7 all_data NA
#> 2361 43G7 2006 83.80202 1.65014790 2006_43G7 all_data NA
#> 2362 43G7 2012 83.80202 3.17711747 2012_43G7 all_data NA
#> 2363 43G7 2013 83.80202 1.79516954 2013_43G7 all_data NA
#> 2364 43G7 2015 83.80202 1.30720808 2015_43G7 all_data NA
#> 2365 43G8 2001 27.86322 7.98989867 2001_43G8 all_data NA
#> 2366 43G8 2002 27.86322 7.83590233 2002_43G8 all_data NA
#> 2367 43G8 2005 27.86322 7.88868175 2005_43G8 all_data NA
#> 2368 43G9 1996 180.66532 0.72873372 1996_43G9 all_data NA
#> 2369 43G9 1998 180.66532 0.37476304 1998_43G9 all_data NA
#> 2370 43G9 2001 180.66532 -0.75326588 2001_43G9 all_data NA
#> 2371 43G9 2002 180.66532 -0.87102563 2002_43G9 all_data NA
#> 2372 43G9 2004 180.66532 0.94879775 2004_43G9 all_data NA
#> 2373 43G9 2006 180.66532 -0.42051901 2006_43G9 all_data NA
#> 2374 43G9 2012 180.66532 -0.77526161 2012_43G9 all_data NA
#> 2375 43G9 2014 180.66532 0.22126613 2014_43G9 all_data NA
#> 2376 43G9 2015 180.66532 0.12570967 2015_43G9 all_data NA
#> 2377 43G9 2016 180.66532 0.35705679 2016_43G9 all_data NA
#> 2378 43G9 2019 180.66532 0.12904535 2019_43G9 all_data NA
#> 2379 43H0 1993 124.04904 6.21293964 1993_43H0 all_data NA
#> 2380 43H0 1994 124.04904 4.01112435 1994_43H0 all_data NA
#> 2381 43H0 1995 124.04904 4.14185979 1995_43H0 all_data NA
#> 2382 43H0 1996 124.04904 5.41035048 1996_43H0 all_data NA
#> 2383 43H0 2005 124.04904 1.60217509 2005_43H0 all_data NA
#> 2384 43H0 2006 124.04904 1.84833761 2006_43H0 all_data NA
#> 2385 43H0 2009 124.04904 2.03411590 2009_43H0 all_data NA
#> 2386 43H0 2015 124.04904 4.13845068 2015_43H0 all_data NA
#> 2387 43H1 1993 23.00000 7.70678900 1993_43H1 all_data NA
#> 2388 43H1 1994 23.00000 7.50578169 1994_43H1 all_data NA
#> 2389 43H1 1995 23.00000 7.44672206 1995_43H1 all_data NA
#> 2390 43H1 1996 23.00000 7.92396006 1996_43H1 all_data NA
#> 2391 43H1 2000 23.00000 7.24706226 2000_43H1 all_data NA
#> 2392 43H1 2004 23.00000 7.56359968 2004_43H1 all_data NA
#> 2393 43H1 2007 23.00000 7.47169877 2007_43H1 all_data NA
#> 2394 43H1 2008 23.00000 7.63645153 2008_43H1 all_data NA
#> 2395 43H1 2016 23.00000 7.90658215 2016_43H1 all_data NA
#> 2396 44G6 1993 50.69443 7.68461121 1993_44G6 all_data NA
#> 2397 44G6 1994 50.69443 7.15367464 1994_44G6 all_data NA
#> 2398 44G6 1995 50.69443 7.82556761 1995_44G6 all_data NA
#> 2399 44G6 1996 50.69443 7.68534888 1996_44G6 all_data NA
#> 2400 44G6 1997 50.69443 7.92742019 1997_44G6 all_data NA
#> 2401 44G6 1998 50.69443 7.66305466 1998_44G6 all_data NA
#> 2402 44G6 1999 50.69443 7.54103293 1999_44G6 all_data NA
#> 2403 44G6 2000 50.69443 7.62919222 2000_44G6 all_data NA
#> 2404 44G6 2001 50.69443 7.55328329 2001_44G6 all_data NA
#> 2405 44G6 2002 50.69443 7.86135257 2002_44G6 all_data NA
#> 2406 44G6 2003 50.69443 7.70986296 2003_44G6 all_data NA
#> 2407 44G6 2004 50.69443 7.94261685 2004_44G6 all_data NA
#> 2408 44G6 2005 50.69443 7.79756388 2005_44G6 all_data NA
#> 2409 44G6 2006 50.69443 6.90588567 2006_44G6 all_data NA
#> 2410 44G6 2007 50.69443 7.54885020 2007_44G6 all_data NA
#> 2411 44G6 2008 50.69443 7.04478808 2008_44G6 all_data NA
#> 2412 44G6 2009 50.69443 8.13603276 2009_44G6 all_data NA
#> 2413 44G6 2010 50.69443 7.28397441 2010_44G6 all_data NA
#> 2414 44G6 2011 50.69443 5.91376869 2011_44G6 all_data NA
#> 2415 44G6 2012 50.69443 7.64334032 2012_44G6 all_data NA
#> 2416 44G6 2013 50.69443 6.54638622 2013_44G6 all_data NA
#> 2417 44G6 2014 50.69443 6.45347120 2014_44G6 all_data NA
#> 2418 44G6 2015 50.69443 5.36760772 2015_44G6 all_data NA
#> 2419 44G6 2016 50.69443 7.57570168 2016_44G6 all_data NA
#> 2420 44G6 2017 50.69443 6.76077353 2017_44G6 all_data NA
#> 2421 44G6 2018 50.69443 7.65040617 2018_44G6 all_data NA
#> 2422 44G6 2019 50.69443 7.46095143 2019_44G6 all_data NA
#> 2423 44G7 1993 122.39409 2.53593139 1993_44G7 all_data NA
#> 2424 44G7 1994 122.39409 1.75851534 1994_44G7 all_data NA
#> 2425 44G7 1995 122.39409 1.85637850 1995_44G7 all_data NA
#> 2426 44G7 1996 122.39409 1.33487202 1996_44G7 all_data NA
#> 2427 44G7 1997 122.39409 1.13249249 1997_44G7 all_data NA
#> 2428 44G7 1998 122.39409 0.78283806 1998_44G7 all_data NA
#> 2429 44G7 1999 122.39409 0.49098807 1999_44G7 all_data NA
#> 2430 44G7 2000 122.39409 0.69738971 2000_44G7 all_data NA
#> 2431 44G7 2001 122.39409 0.03574116 2001_44G7 all_data NA
#> 2432 44G7 2002 122.39409 0.42775643 2002_44G7 all_data NA
#> 2433 44G7 2003 122.39409 0.14073929 2003_44G7 all_data NA
#> 2434 44G7 2004 122.39409 0.15161280 2004_44G7 all_data NA
#> 2435 44G7 2005 122.39409 0.51489051 2005_44G7 all_data NA
#> 2436 44G7 2006 122.39409 -0.35247031 2006_44G7 all_data NA
#> 2437 44G7 2007 122.39409 -0.23608853 2007_44G7 all_data NA
#> 2438 44G7 2008 122.39409 0.05998680 2008_44G7 all_data NA
#> 2439 44G7 2009 122.39409 -0.19124795 2009_44G7 all_data NA
#> 2440 44G7 2010 122.39409 -0.33215064 2010_44G7 all_data NA
#> 2441 44G7 2011 122.39409 -0.10581522 2011_44G7 all_data NA
#> 2442 44G7 2012 122.39409 0.26968857 2012_44G7 all_data NA
#> 2443 44G7 2013 122.39409 0.32446291 2013_44G7 all_data NA
#> 2444 44G7 2014 122.39409 0.31990042 2014_44G7 all_data NA
#> 2445 44G7 2015 122.39409 -0.21167954 2015_44G7 all_data NA
#> 2446 44G7 2016 122.39409 0.27993611 2016_44G7 all_data NA
#> 2447 44G7 2017 122.39409 -0.19691194 2017_44G7 all_data NA
#> 2448 44G7 2018 122.39409 0.18718077 2018_44G7 all_data NA
#> 2449 44G7 2019 122.39409 1.50757281 2019_44G7 all_data NA
#> 2450 44G8 1993 110.76247 4.00019389 1993_44G8 all_data NA
#> 2451 44G8 1995 110.76247 3.17267008 1995_44G8 all_data NA
#> 2452 44G8 1996 110.76247 2.04626046 1996_44G8 all_data NA
#> 2453 44G8 1997 110.76247 1.73724744 1997_44G8 all_data NA
#> 2454 44G8 1998 110.76247 1.74204664 1998_44G8 all_data NA
#> 2455 44G8 1999 110.76247 1.28251654 1999_44G8 all_data NA
#> 2456 44G8 2000 110.76247 1.54252815 2000_44G8 all_data NA
#> 2457 44G8 2001 110.76247 0.87894721 2001_44G8 all_data NA
#> 2458 44G8 2002 110.76247 1.44048631 2002_44G8 all_data NA
#> 2459 44G8 2003 110.76247 0.96958954 2003_44G8 all_data NA
#> 2460 44G8 2004 110.76247 0.56581321 2004_44G8 all_data NA
#> 2461 44G8 2005 110.76247 1.54673008 2005_44G8 all_data NA
#> 2462 44G8 2006 110.76247 0.36692755 2006_44G8 all_data NA
#> 2463 44G8 2007 110.76247 0.65092243 2007_44G8 all_data NA
#> 2464 44G8 2008 110.76247 0.72267286 2008_44G8 all_data NA
#> 2465 44G8 2009 110.76247 0.60484651 2009_44G8 all_data NA
#> 2466 44G8 2010 110.76247 0.74011035 2010_44G8 all_data NA
#> 2467 44G8 2011 110.76247 0.41831385 2011_44G8 all_data NA
#> 2468 44G8 2012 110.76247 0.92920334 2012_44G8 all_data NA
#> 2469 44G8 2013 110.76247 1.20956138 2013_44G8 all_data NA
#> 2470 44G8 2014 110.76247 1.06620441 2014_44G8 all_data NA
#> 2471 44G8 2015 110.76247 0.59577904 2015_44G8 all_data NA
#> 2472 44G8 2016 110.76247 0.87581148 2016_44G8 all_data NA
#> 2473 44G8 2017 110.76247 0.45060318 2017_44G8 all_data NA
#> 2474 44G8 2018 110.76247 0.95974925 2018_44G8 all_data NA
#> 2475 44G8 2019 110.76247 1.33883231 2019_44G8 all_data NA
#> 2476 44G9 1996 98.95766 2.65524102 1996_44G9 all_data NA
#> 2477 44G9 2002 98.95766 2.72966988 2002_44G9 all_data NA
#> 2478 44G9 2003 98.95766 1.91938063 2003_44G9 all_data NA
#> 2479 44G9 2007 98.95766 1.75243659 2007_44G9 all_data NA
#> 2480 44G9 2008 98.95766 1.77161590 2008_44G9 all_data NA
#> 2481 44H0 1993 139.00000 2.53325598 1993_44H0 all_data NA
#> 2482 44H0 1994 139.00000 1.56110484 1994_44H0 all_data NA
#> 2483 44H0 1995 139.00000 1.87007058 1995_44H0 all_data NA
#> 2484 44H0 1996 139.00000 3.41882488 1996_44H0 all_data NA
#> 2485 44H0 1997 139.00000 2.13308984 1997_44H0 all_data NA
#> 2486 44H0 1998 139.00000 1.59638851 1998_44H0 all_data NA
#> 2487 44H0 1999 139.00000 0.54361717 1999_44H0 all_data NA
#> 2488 44H0 2000 139.00000 0.77099570 2000_44H0 all_data NA
#> 2489 44H0 2001 139.00000 0.17271389 2001_44H0 all_data NA
#> 2490 44H0 2002 139.00000 0.32930723 2002_44H0 all_data NA
#> 2491 44H0 2003 139.00000 0.90104746 2003_44H0 all_data NA
#> 2492 44H0 2004 139.00000 0.98480765 2004_44H0 all_data NA
#> 2493 44H0 2005 139.00000 1.12487792 2005_44H0 all_data NA
#> 2494 44H0 2006 139.00000 -0.03580202 2006_44H0 all_data NA
#> 2495 44H0 2007 139.00000 0.34600610 2007_44H0 all_data NA
#> 2496 44H0 2008 139.00000 0.43651244 2008_44H0 all_data NA
#> 2497 44H0 2011 139.00000 -0.39070845 2011_44H0 all_data NA
#> 2498 44H0 2012 139.00000 -0.14953050 2012_44H0 all_data NA
#> 2499 44H0 2013 139.00000 0.80105049 2013_44H0 all_data NA
#> 2500 44H0 2014 139.00000 1.99644364 2014_44H0 all_data NA
#> 2501 44H0 2016 139.00000 3.98026624 2016_44H0 all_data NA
#> 2502 44H0 2017 139.00000 2.45678972 2017_44H0 all_data NA
#> 2503 44H0 2018 139.00000 0.93826535 2018_44H0 all_data NA
#> 2504 44H1 1993 30.23161 7.66812010 1993_44H1 all_data NA
#> 2505 44H1 1994 30.23161 7.48463470 1994_44H1 all_data NA
#> 2506 44H1 1995 30.23161 7.64794919 1995_44H1 all_data NA
#> 2507 44H1 1996 30.23161 7.87837719 1996_44H1 all_data NA
#> 2508 44H1 1999 30.23161 7.83634315 1999_44H1 all_data NA
#> 2509 44H1 2001 30.23161 7.62386028 2001_44H1 all_data NA
#> 2510 44H1 2002 30.23161 8.11188027 2002_44H1 all_data NA
#> 2511 44H1 2003 30.23161 7.45708704 2003_44H1 all_data NA
#> 2512 44H1 2004 30.23161 7.60755997 2004_44H1 all_data NA
#> 2513 44H1 2006 30.23161 7.61031516 2006_44H1 all_data NA
#> 2514 37G2 1993 14.28574 4.20511419 1993_37G2 niche_data NA
#> 2515 37G2 1994 14.28574 6.63390811 1994_37G2 niche_data NA
#> 2516 37G2 1995 14.28574 5.96333062 1995_37G2 niche_data NA
#> 2517 37G2 1996 14.28574 6.17467651 1996_37G2 niche_data NA
#> 2518 37G2 1997 14.28574 6.30160302 1997_37G2 niche_data NA
#> 2519 37G2 1998 14.28574 6.68297993 1998_37G2 niche_data NA
#> 2520 37G2 1999 14.28574 5.75838467 1999_37G2 niche_data NA
#> 2521 37G2 2000 14.28574 5.27109639 2000_37G2 niche_data NA
#> 2522 37G2 2001 14.28574 6.09256122 2001_37G2 niche_data NA
#> 2523 37G2 2004 14.28574 6.04276928 2004_37G2 niche_data NA
#> 2524 37G2 2007 14.28574 6.29931225 2007_37G2 niche_data NA
#> 2525 37G2 2018 14.28574 6.57808544 2018_37G2 niche_data NA
#> 2526 37G3 1993 15.00000 7.76069318 1993_37G3 niche_data NA
#> 2527 37G3 1995 15.00000 7.17451859 1995_37G3 niche_data NA
#> 2528 37G3 1996 15.00000 7.15662002 1996_37G3 niche_data NA
#> 2529 37G3 1997 15.00000 7.23679535 1997_37G3 niche_data NA
#> 2530 37G3 1998 15.00000 7.85840117 1998_37G3 niche_data NA
#> 2531 37G3 1999 15.00000 6.86896506 1999_37G3 niche_data NA
#> 2532 37G3 2000 15.00000 7.12327788 2000_37G3 niche_data NA
#> 2533 37G3 2001 15.00000 7.20570204 2001_37G3 niche_data NA
#> 2534 37G3 2002 15.00000 7.56770930 2002_37G3 niche_data NA
#> 2535 37G3 2003 15.00000 7.22337769 2003_37G3 niche_data NA
#> 2536 37G3 2007 15.00000 7.28948577 2007_37G3 niche_data NA
#> 2537 37G3 2017 15.00000 7.67859773 2017_37G3 niche_data NA
#> 2538 37G4 1994 13.00000 7.58559337 1994_37G4 niche_data NA
#> 2539 37G4 1995 13.00000 7.39707206 1995_37G4 niche_data NA
#> 2540 37G4 1996 13.00000 7.53205651 1996_37G4 niche_data NA
#> 2541 37G4 1998 13.00000 7.83061633 1998_37G4 niche_data NA
#> 2542 37G4 1999 13.00000 7.44677978 1999_37G4 niche_data NA
#> 2543 37G4 2000 13.00000 7.48420390 2000_37G4 niche_data NA
#> 2544 37G4 2001 13.00000 7.45198553 2001_37G4 niche_data NA
#> 2545 37G4 2003 13.00000 7.32028555 2003_37G4 niche_data NA
#> 2546 37G4 2010 13.00000 7.83301839 2010_37G4 niche_data NA
#> 2547 37G5 1993 20.00000 7.60992421 1993_37G5 niche_data NA
#> 2548 37G5 1994 20.00000 7.48136077 1994_37G5 niche_data NA
#> 2549 37G5 1995 20.00000 7.34416200 1995_37G5 niche_data NA
#> 2550 37G5 1996 20.00000 7.61639668 1996_37G5 niche_data NA
#> 2551 37G5 1997 20.00000 7.29925896 1997_37G5 niche_data NA
#> 2552 37G5 1998 20.00000 7.65867811 1998_37G5 niche_data NA
#> 2553 37G5 1999 20.00000 7.53702702 1999_37G5 niche_data NA
#> 2554 37G5 2000 20.00000 7.47179504 2000_37G5 niche_data NA
#> 2555 37G5 2001 20.00000 7.45119157 2001_37G5 niche_data NA
#> 2556 37G5 2006 20.00000 7.30919856 2006_37G5 niche_data NA
#> 2557 37G6 1993 18.00000 7.49108204 1993_37G6 niche_data NA
#> 2558 37G6 1994 18.00000 7.39499081 1994_37G6 niche_data NA
#> 2559 37G6 1995 18.00000 7.27448384 1995_37G6 niche_data NA
#> 2560 37G6 1996 18.00000 7.74132245 1996_37G6 niche_data NA
#> 2561 37G6 1997 18.00000 7.22383697 1997_37G6 niche_data NA
#> 2562 37G6 1998 18.00000 7.71196467 1998_37G6 niche_data NA
#> 2563 37G6 1999 18.00000 7.64088136 1999_37G6 niche_data NA
#> 2564 37G6 2000 18.00000 7.39410181 2000_37G6 niche_data NA
#> 2565 37G6 2001 18.00000 7.62648277 2001_37G6 niche_data NA
#> 2566 37G6 2002 18.00000 7.54646011 2002_37G6 niche_data NA
#> 2567 37G6 2004 18.00000 7.68284044 2004_37G6 niche_data NA
#> 2568 37G6 2005 18.00000 7.47241853 2005_37G6 niche_data NA
#> 2569 37G6 2006 18.00000 7.46757608 2006_37G6 niche_data NA
#> 2570 37G6 2007 18.00000 7.28740493 2007_37G6 niche_data NA
#> 2571 37G6 2015 18.00000 7.55594598 2015_37G6 niche_data NA
#> 2572 37G6 2016 18.00000 7.75680214 2016_37G6 niche_data NA
#> 2573 37G8 1993 20.86536 7.01664324 1993_37G8 niche_data NA
#> 2574 37G8 1994 20.86536 6.15052770 1994_37G8 niche_data NA
#> 2575 37G8 1995 20.86536 6.34913967 1995_37G8 niche_data NA
#> 2576 37G8 1996 20.86536 6.53975753 1996_37G8 niche_data NA
#> 2577 37G8 1997 20.86536 6.51565765 1997_37G8 niche_data NA
#> 2578 37G8 1998 20.86536 6.23302818 1998_37G8 niche_data NA
#> 2579 37G8 1999 20.86536 6.44170361 1999_37G8 niche_data NA
#> 2580 37G8 2000 20.86536 6.26792875 2000_37G8 niche_data NA
#> 2581 37G8 2001 20.86536 6.82311689 2001_37G8 niche_data NA
#> 2582 37G8 2002 20.86536 6.88362871 2002_37G8 niche_data NA
#> 2583 37G8 2003 20.86536 6.64862647 2003_37G8 niche_data NA
#> 2584 37G8 2004 20.86536 7.13540801 2004_37G8 niche_data NA
#> 2585 37G8 2005 20.86536 6.98302323 2005_37G8 niche_data NA
#> 2586 37G8 2006 20.86536 6.88736045 2006_37G8 niche_data NA
#> 2587 37G8 2008 20.86536 6.92182290 2008_37G8 niche_data NA
#> 2588 37G8 2009 20.86536 6.63211300 2009_37G8 niche_data NA
#> 2589 37G8 2010 20.86536 7.28464351 2010_37G8 niche_data NA
#> 2590 37G8 2011 20.86536 6.58978230 2011_37G8 niche_data NA
#> 2591 37G8 2012 20.86536 6.71990256 2012_37G8 niche_data NA
#> 2592 37G8 2013 20.86536 6.93181939 2013_37G8 niche_data NA
#> 2593 37G8 2016 20.86536 7.20219968 2016_37G8 niche_data NA
#> 2594 37G8 2017 20.86536 7.08395984 2017_37G8 niche_data NA
#> 2595 37G8 2018 20.86536 6.72066409 2018_37G8 niche_data NA
#> 2596 37G8 2019 20.86536 7.00984767 2019_37G8 niche_data NA
#> 2597 37G9 1993 43.07566 7.06676271 1993_37G9 niche_data NA
#> 2598 37G9 1994 43.07566 6.19283092 1994_37G9 niche_data NA
#> 2599 37G9 1995 43.07566 6.52132416 1995_37G9 niche_data NA
#> 2600 37G9 1996 43.07566 6.79768933 1996_37G9 niche_data NA
#> 2601 37G9 1997 43.07566 6.16910845 1997_37G9 niche_data NA
#> 2602 37G9 1998 43.07566 6.08435426 1998_37G9 niche_data NA
#> 2603 37G9 1999 43.07566 6.38760333 1999_37G9 niche_data NA
#> 2604 37G9 2000 43.07566 6.36699536 2000_37G9 niche_data NA
#> 2605 37G9 2001 43.07566 6.68125086 2001_37G9 niche_data NA
#> 2606 37G9 2002 43.07566 6.91182196 2002_37G9 niche_data NA
#> 2607 37G9 2014 43.07566 6.70335729 2014_37G9 niche_data NA
#> 2608 38G2 1993 17.00000 6.05552989 1993_38G2 niche_data NA
#> 2609 38G2 1994 17.00000 6.82655030 1994_38G2 niche_data NA
#> 2610 38G2 1995 17.00000 6.78852044 1995_38G2 niche_data NA
#> 2611 38G2 1996 17.00000 6.85859963 1996_38G2 niche_data NA
#> 2612 38G2 1997 17.00000 7.05134306 1997_38G2 niche_data NA
#> 2613 38G2 1998 17.00000 7.32619512 1998_38G2 niche_data NA
#> 2614 38G5 1993 61.00000 3.96560658 1993_38G5 niche_data NA
#> 2615 38G5 1994 61.20802 3.20767527 1994_38G5 niche_data NA
#> 2616 38G5 1995 57.00000 4.17131488 1995_38G5 niche_data NA
#> 2617 38G5 1996 61.20802 4.82126540 1996_38G5 niche_data NA
#> 2618 38G5 1997 57.00000 3.63807618 1997_38G5 niche_data NA
#> 2619 38G5 1998 57.00000 4.37412120 1998_38G5 niche_data NA
#> 2620 38G5 1999 58.00000 3.74897011 1999_38G5 niche_data NA
#> 2621 38G5 2001 57.96658 4.25670554 2001_38G5 niche_data NA
#> 2622 38G5 2002 57.93315 5.05532741 2002_38G5 niche_data NA
#> 2623 38G6 1993 26.52553 7.44933723 1993_38G6 niche_data NA
#> 2624 38G6 1994 26.52553 7.39133176 1994_38G6 niche_data NA
#> 2625 38G6 1995 26.43109 7.21344589 1995_38G6 niche_data NA
#> 2626 38G6 1996 26.52553 7.64821966 1996_38G6 niche_data NA
#> 2627 38G6 1997 26.52553 7.23463595 1997_38G6 niche_data NA
#> 2628 38G6 1998 26.43109 7.59565926 1998_38G6 niche_data NA
#> 2629 38G6 1999 26.43109 7.59284166 1999_38G6 niche_data NA
#> 2630 38G6 2001 26.43109 7.54168520 2001_38G6 niche_data NA
#> 2631 38G7 1993 21.00000 7.66297490 1993_38G7 niche_data NA
#> 2632 38G7 1994 21.00000 7.62870196 1994_38G7 niche_data NA
#> 2633 38G7 1995 21.00000 7.34043636 1995_38G7 niche_data NA
#> 2634 38G7 1996 21.00000 7.72085964 1996_38G7 niche_data NA
#> 2635 38G7 1997 21.00000 7.50529819 1997_38G7 niche_data NA
#> 2636 38G7 1998 21.00000 7.75350536 1998_38G7 niche_data NA
#> 2637 38G7 1999 21.00000 7.75870685 1999_38G7 niche_data NA
#> 2638 38G7 2000 21.00000 7.48371628 2000_38G7 niche_data NA
#> 2639 38G7 2001 21.00000 7.69145452 2001_38G7 niche_data NA
#> 2640 38G7 2003 21.00000 7.39710867 2003_38G7 niche_data NA
#> 2641 38G7 2006 21.00000 7.51662002 2006_38G7 niche_data NA
#> 2642 38G8 1993 28.72128 7.12363056 1993_38G8 niche_data NA
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#> 2644 38G8 1995 28.72128 6.75633193 1995_38G8 niche_data NA
#> 2645 38G8 1996 28.72128 7.10185434 1996_38G8 niche_data NA
#> 2646 38G8 1997 28.72128 6.74932606 1997_38G8 niche_data NA
#> 2647 38G8 1998 28.72128 6.59850014 1998_38G8 niche_data NA
#> 2648 38G8 1999 28.72128 7.14287638 1999_38G8 niche_data NA
#> 2649 38G8 2000 28.72128 6.61911035 2000_38G8 niche_data NA
#> 2650 38G8 2001 28.72128 7.11668589 2001_38G8 niche_data NA
#> 2651 38G8 2002 28.72128 7.13619961 2002_38G8 niche_data NA
#> 2652 38G9 1993 61.59609 6.53527481 1993_38G9 niche_data NA
#> 2653 38G9 1994 61.59609 6.32189758 1994_38G9 niche_data NA
#> 2654 38G9 1995 61.59609 6.66182950 1995_38G9 niche_data NA
#> 2655 38G9 1996 61.59609 7.57132352 1996_38G9 niche_data NA
#> 2656 38G9 1997 59.60221 5.02414804 1997_38G9 niche_data NA
#> 2657 38G9 1998 61.59609 4.90418089 1998_38G9 niche_data NA
#> 2658 38G9 1999 61.59609 5.38034400 1999_38G9 niche_data NA
#> 2659 38G9 2000 53.88420 6.14271727 2000_38G9 niche_data NA
#> 2660 38G9 2001 61.59609 6.33206723 2001_38G9 niche_data NA
#> 2661 38G9 2002 61.59609 6.50562502 2002_38G9 niche_data NA
#> 2662 38G9 2009 61.59609 5.79178332 2009_38G9 niche_data NA
#> 2663 38H0 1993 25.40843 8.11768631 1993_38H0 niche_data NA
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#> 2665 38H0 1995 25.40843 7.57679975 1995_38H0 niche_data NA
#> 2666 38H0 1996 25.40843 7.50906781 1996_38H0 niche_data NA
#> 2667 38H0 1997 25.40843 7.30467319 1997_38H0 niche_data NA
#> 2668 38H0 1998 25.40843 7.90287733 1998_38H0 niche_data NA
#> 2669 38H0 1999 25.40843 7.73133571 1999_38H0 niche_data NA
#> 2670 38H0 2000 25.40843 7.48040935 2000_38H0 niche_data NA
#> 2671 38H0 2001 25.40843 7.75845265 2001_38H0 niche_data NA
#> 2672 38H0 2002 25.40843 7.77892181 2002_38H0 niche_data NA
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#> 2675 38H0 2005 25.40843 7.44535883 2005_38H0 niche_data NA
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#> 2677 38H0 2007 25.40843 7.60890696 2007_38H0 niche_data NA
#> 2678 38H0 2008 25.40843 7.63600128 2008_38H0 niche_data NA
#> 2679 38H0 2009 25.40843 7.76965866 2009_38H0 niche_data NA
#> 2680 38H0 2010 25.40843 8.06326072 2010_38H0 niche_data NA
#> 2681 38H0 2011 25.40843 7.75620335 2011_38H0 niche_data NA
#> 2682 38H0 2012 25.40843 7.90984354 2012_38H0 niche_data NA
#> 2683 38H0 2013 25.40843 7.65943229 2013_38H0 niche_data NA
#> 2684 38H0 2014 25.40843 7.50772141 2014_38H0 niche_data NA
#> 2685 38H0 2015 25.40843 7.86688978 2015_38H0 niche_data NA
#> 2686 38H0 2016 25.40843 8.25953099 2016_38H0 niche_data NA
#> 2687 38H0 2017 25.40843 8.07327909 2017_38H0 niche_data NA
#> 2688 38H0 2018 25.40843 7.98754044 2018_38H0 niche_data NA
#> 2689 38H0 2019 25.40843 7.97582971 2019_38H0 niche_data NA
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#> 2692 38H1 1995 19.05070 7.16752888 1995_38H1 niche_data NA
#> 2693 38H1 1996 19.05070 6.93238265 1996_38H1 niche_data NA
#> 2694 38H1 1997 19.05070 7.28553821 1997_38H1 niche_data NA
#> 2695 38H1 1998 19.05070 7.80666691 1998_38H1 niche_data NA
#> 2696 38H1 1999 19.05070 7.29755746 1999_38H1 niche_data NA
#> 2697 38H1 2000 19.05070 7.11444096 2000_38H1 niche_data NA
#> 2698 38H1 2001 19.05070 7.46701962 2001_38H1 niche_data NA
#> 2699 38H1 2002 19.05070 7.74018533 2002_38H1 niche_data NA
#> 2700 38H1 2003 19.05070 7.72443087 2003_38H1 niche_data NA
#> 2701 38H1 2004 19.05070 7.25328379 2004_38H1 niche_data NA
#> 2702 38H1 2005 19.05070 6.97672099 2005_38H1 niche_data NA
#> 2703 38H1 2006 19.05070 6.90305419 2006_38H1 niche_data NA
#> 2704 38H1 2007 19.05070 7.49408782 2007_38H1 niche_data NA
#> 2705 38H1 2008 19.05070 7.48430650 2008_38H1 niche_data NA
#> 2706 38H1 2009 19.05070 7.72732625 2009_38H1 niche_data NA
#> 2707 38H1 2010 19.05070 7.92293670 2010_38H1 niche_data NA
#> 2708 38H1 2011 19.05070 7.74005428 2011_38H1 niche_data NA
#> 2709 38H1 2012 19.05070 7.87143226 2012_38H1 niche_data NA
#> 2710 38H1 2013 19.05070 7.78247626 2013_38H1 niche_data NA
#> 2711 38H1 2014 19.05070 7.50278451 2014_38H1 niche_data NA
#> 2712 38H1 2015 19.05070 7.84719548 2015_38H1 niche_data NA
#> 2713 38H1 2016 19.05070 8.26214908 2016_38H1 niche_data NA
#> 2714 38H1 2017 19.05070 8.06476599 2017_38H1 niche_data NA
#> 2715 38H1 2018 19.05070 7.96728259 2018_38H1 niche_data NA
#> 2716 38H1 2019 19.05070 8.01308126 2019_38H1 niche_data NA
#> 2717 39G2 1993 19.00000 7.29217886 1993_39G2 niche_data NA
#> 2718 39G2 1994 19.00000 7.08028842 1994_39G2 niche_data NA
#> 2719 39G2 1995 19.00000 7.12534761 1995_39G2 niche_data NA
#> 2720 39G2 1996 19.00000 7.23473904 1996_39G2 niche_data NA
#> 2721 39G2 1997 19.00000 7.52556012 1997_39G2 niche_data NA
#> 2722 39G2 1998 19.00000 7.44898598 1998_39G2 niche_data NA
#> 2723 39G2 1999 19.00000 7.23632361 1999_39G2 niche_data NA
#> 2724 39G2 2001 19.00000 7.73925850 2001_39G2 niche_data NA
#> 2725 39G2 2003 19.00000 7.30094799 2003_39G2 niche_data NA
#> 2726 39G2 2004 19.00000 7.31416437 2004_39G2 niche_data NA
#> 2727 39G2 2005 19.00000 6.86376368 2005_39G2 niche_data NA
#> 2728 39G2 2009 19.00000 7.25172088 2009_39G2 niche_data NA
#> 2729 39G2 2010 19.00000 7.57748679 2010_39G2 niche_data NA
#> 2730 39G2 2012 19.00000 7.53586179 2012_39G2 niche_data NA
#> 2731 39G5 1994 77.91940 2.21667040 1994_39G5 niche_data NA
#> 2732 39G5 1995 67.00024 4.09008511 1995_39G5 niche_data NA
#> 2733 39G5 1997 76.01884 2.50689933 1997_39G5 niche_data NA
#> 2734 39G5 1998 70.82971 4.28781619 1998_39G5 niche_data NA
#> 2735 39G5 2000 65.31756 3.97266155 2000_39G5 niche_data NA
#> 2736 39G5 2001 78.00000 2.95947631 2001_39G5 niche_data NA
#> 2737 39G5 2014 75.62254 2.26101847 2014_39G5 niche_data NA
#> 2738 39G6 1993 66.00000 4.71573259 1993_39G6 niche_data NA
#> 2739 39G6 1994 62.09842 3.88251945 1994_39G6 niche_data NA
#> 2740 39G6 1995 56.53212 4.74775274 1995_39G6 niche_data NA
#> 2741 39G6 1996 63.79890 5.04541013 1996_39G6 niche_data NA
#> 2742 39G6 1998 60.00000 4.54512907 1998_39G6 niche_data NA
#> 2743 39G6 1999 58.00000 4.54083026 1999_39G6 niche_data NA
#> 2744 39G6 2001 58.18382 4.61939221 2001_39G6 niche_data NA
#> 2745 39G6 2002 60.06716 4.57397097 2002_39G6 niche_data NA
#> 2746 39G7 1993 52.79956 6.67415281 1993_39G7 niche_data NA
#> 2747 39G7 1994 52.79956 6.55919462 1994_39G7 niche_data NA
#> 2748 39G7 1995 52.79956 6.40591612 1995_39G7 niche_data NA
#> 2749 39G7 1996 52.79956 6.98277240 1996_39G7 niche_data NA
#> 2750 39G7 1997 52.79956 6.36291991 1997_39G7 niche_data NA
#> 2751 39G7 1998 52.79956 6.10955028 1998_39G7 niche_data NA
#> 2752 39G7 1999 52.79956 6.39000604 1999_39G7 niche_data NA
#> 2753 39G7 2001 52.79956 6.58363755 2001_39G7 niche_data NA
#> 2754 39G8 1993 88.00000 5.06969009 1993_39G8 niche_data NA
#> 2755 39G8 1994 88.00000 4.08638021 1994_39G8 niche_data NA
#> 2756 39G8 1995 88.00000 4.31855172 1995_39G8 niche_data NA
#> 2757 39G8 1996 88.00000 5.13901725 1996_39G8 niche_data NA
#> 2758 39G8 1997 88.00000 4.44219563 1997_39G8 niche_data NA
#> 2759 39G8 1998 88.00000 3.74763196 1998_39G8 niche_data NA
#> 2760 39G8 1999 88.00000 3.29362047 1999_39G8 niche_data NA
#> 2761 39G8 2002 88.00000 4.12551365 2002_39G8 niche_data NA
#> 2762 39G9 1993 88.00000 5.21721358 1993_39G9 niche_data NA
#> 2763 39G9 1994 88.00000 4.26600259 1994_39G9 niche_data NA
#> 2764 39G9 1995 86.05812 4.94106030 1995_39G9 niche_data NA
#> 2765 39G9 1996 88.00000 5.29567123 1996_39G9 niche_data NA
#> 2766 39G9 1997 86.00000 3.53934694 1997_39G9 niche_data NA
#> 2767 39G9 1998 82.50000 3.51990253 1998_39G9 niche_data NA
#> 2768 39G9 1999 81.12589 3.51589365 1999_39G9 niche_data NA
#> 2769 39G9 2000 83.00000 4.04939629 2000_39G9 niche_data NA
#> 2770 39G9 2001 83.00000 3.60633832 2001_39G9 niche_data NA
#> 2771 39G9 2002 86.00000 4.55020559 2002_39G9 niche_data NA
#> 2772 39G9 2009 86.00000 3.69063626 2009_39G9 niche_data NA
#> 2773 39G9 2012 86.00000 3.89420214 2012_39G9 niche_data NA
#> 2774 39G9 2013 86.00000 4.42933517 2013_39G9 niche_data NA
#> 2775 39G9 2019 88.00000 4.39378952 2019_39G9 niche_data NA
#> 2776 39H0 1993 28.00000 7.66373752 1993_39H0 niche_data NA
#> 2777 39H0 1994 28.00000 7.53838912 1994_39H0 niche_data NA
#> 2778 39H0 1995 28.00000 7.46610932 1995_39H0 niche_data NA
#> 2779 39H0 1996 28.00000 7.80617631 1996_39H0 niche_data NA
#> 2780 39H0 1997 28.00000 7.24553021 1997_39H0 niche_data NA
#> 2781 39H0 1998 28.00000 7.61357032 1998_39H0 niche_data NA
#> 2782 39H0 1999 28.00000 7.71378980 1999_39H0 niche_data NA
#> 2783 39H0 2000 28.00000 7.55370793 2000_39H0 niche_data NA
#> 2784 39H0 2001 28.00000 7.63957830 2001_39H0 niche_data NA
#> 2785 39H0 2002 28.00000 7.69008934 2002_39H0 niche_data NA
#> 2786 39H0 2012 28.00000 7.41403360 2012_39H0 niche_data NA
#> 2787 39H0 2013 28.00000 7.55982960 2013_39H0 niche_data NA
#> 2788 39H0 2014 28.00000 7.41652280 2014_39H0 niche_data NA
#> 2789 39H0 2018 28.00000 7.16969025 2018_39H0 niche_data NA
#> 2790 39H1 1993 16.94447 7.92848003 1993_39H1 niche_data NA
#> 2791 39H1 1994 16.94447 6.76520130 1994_39H1 niche_data NA
#> 2792 39H1 1995 16.94447 6.84790373 1995_39H1 niche_data NA
#> 2793 39H1 1996 16.94447 6.30069030 1996_39H1 niche_data NA
#> 2794 39H1 1997 16.94447 7.15760498 1997_39H1 niche_data NA
#> 2795 39H1 1998 16.94447 7.71061296 1998_39H1 niche_data NA
#> 2796 39H1 1999 16.94447 6.64860804 1999_39H1 niche_data NA
#> 2797 39H1 2000 16.94447 6.88042867 2000_39H1 niche_data NA
#> 2798 39H1 2001 16.94447 7.28687867 2001_39H1 niche_data NA
#> 2799 39H1 2002 16.94447 7.45305159 2002_39H1 niche_data NA
#> 2800 39H1 2003 16.94447 7.22602350 2003_39H1 niche_data NA
#> 2801 39H1 2004 16.94447 7.09008829 2004_39H1 niche_data NA
#> 2802 39H1 2005 16.94447 6.83502359 2005_39H1 niche_data NA
#> 2803 39H1 2006 16.94447 6.97542183 2006_39H1 niche_data NA
#> 2804 39H1 2007 16.94447 6.89413450 2007_39H1 niche_data NA
#> 2805 39H1 2008 16.94447 7.13092141 2008_39H1 niche_data NA
#> 2806 39H1 2009 16.94447 7.63121452 2009_39H1 niche_data NA
#> 2807 39H1 2010 16.94447 7.66135109 2010_39H1 niche_data NA
#> 2808 39H1 2011 16.94447 7.35640886 2011_39H1 niche_data NA
#> 2809 39H1 2012 16.94447 7.51877304 2012_39H1 niche_data NA
#> 2810 39H1 2013 16.94447 7.23910587 2013_39H1 niche_data NA
#> 2811 39H1 2014 16.94447 7.06062064 2014_39H1 niche_data NA
#> 2812 39H1 2015 16.94447 7.26020262 2015_39H1 niche_data NA
#> 2813 39H1 2016 16.94447 7.85906982 2016_39H1 niche_data NA
#> 2814 39H1 2017 16.94447 7.95343597 2017_39H1 niche_data NA
#> 2815 39H1 2018 16.94447 7.17325372 2018_39H1 niche_data NA
#> 2816 39H1 2019 16.94447 7.63963868 2019_39H1 niche_data NA
#> 2817 40G8 1993 90.00000 5.18663251 1993_40G8 niche_data NA
#> 2818 40G8 1996 90.00000 4.73106145 1996_40G8 niche_data NA
#> 2819 40G8 1998 90.00000 3.13343910 1998_40G8 niche_data NA
#> 2820 40G8 1999 90.00000 2.65307058 1999_40G8 niche_data NA
#> 2821 40G8 2000 90.00000 3.25554205 2000_40G8 niche_data NA
#> 2822 40G8 2002 90.00000 3.45824007 2002_40G8 niche_data NA
#> 2823 40G8 2003 90.00000 3.88511078 2003_40G8 niche_data NA
#> 2824 40G8 2010 90.00000 2.70500445 2010_40G8 niche_data NA
#> 2825 40G8 2012 90.00000 3.70618155 2012_40G8 niche_data NA
#> 2826 40G8 2018 90.00000 3.49760928 2018_40G8 niche_data NA
#> 2827 40G9 1993 82.00000 5.54895784 1993_40G9 niche_data NA
#> 2828 40G9 1994 82.00000 4.27678489 1994_40G9 niche_data NA
#> 2829 40G9 1995 82.00000 4.56483732 1995_40G9 niche_data NA
#> 2830 40G9 1998 78.06110 2.91320160 1998_40G9 niche_data NA
#> 2831 40G9 1999 75.00000 2.94680805 1999_40G9 niche_data NA
#> 2832 40G9 2001 77.88208 3.05396092 2001_40G9 niche_data NA
#> 2833 40G9 2002 82.00000 3.41270843 2002_40G9 niche_data NA
#> 2834 40G9 2006 82.00000 3.51193250 2006_40G9 niche_data NA
#> 2835 40G9 2009 79.00000 3.14256950 2009_40G9 niche_data NA
#> 2836 40G9 2011 78.00000 3.23689323 2011_40G9 niche_data NA
#> 2837 40G9 2012 79.00000 3.45831947 2012_40G9 niche_data NA
#> 2838 40G9 2013 80.22516 3.68908618 2013_40G9 niche_data NA
#> 2839 40G9 2014 82.00000 3.38371457 2014_40G9 niche_data NA
#> 2840 40G9 2015 81.00000 4.11646475 2015_40G9 niche_data NA
#> 2841 40G9 2018 78.99048 3.40002225 2018_40G9 niche_data NA
#> 2842 40G9 2019 82.00000 3.64282224 2019_40G9 niche_data NA
#> 2843 40H0 1993 44.00000 7.17913771 1993_40H0 niche_data NA
#> 2844 40H0 1994 44.00000 7.24683351 1994_40H0 niche_data NA
#> 2845 40H0 1995 44.00000 7.22433971 1995_40H0 niche_data NA
#> 2846 40H0 1996 44.00000 7.62571470 1996_40H0 niche_data NA
#> 2847 40H0 1997 44.00000 7.39709618 1997_40H0 niche_data NA
#> 2848 40H0 1998 44.00000 7.40917676 1998_40H0 niche_data NA
#> 2849 40H0 1999 43.00000 7.57169034 1999_40H0 niche_data NA
#> 2850 40H0 2000 44.00000 7.34911329 2000_40H0 niche_data NA
#> 2851 40H0 2001 44.00000 7.41942531 2001_40H0 niche_data NA
#> 2852 40H0 2002 44.00000 7.67278711 2002_40H0 niche_data NA
#> 2853 40H1 1993 13.18982 7.44742885 1993_40H1 niche_data NA
#> 2854 40H1 1994 13.18982 7.23045131 1994_40H1 niche_data NA
#> 2855 40H1 1995 13.18982 7.23908378 1995_40H1 niche_data NA
#> 2856 40H1 1996 13.18982 7.67636949 1996_40H1 niche_data NA
#> 2857 40H1 1997 13.18982 7.18711157 1997_40H1 niche_data NA
#> 2858 40H1 1998 13.18982 7.57713233 1998_40H1 niche_data NA
#> 2859 40H1 1999 13.18982 7.48610539 1999_40H1 niche_data NA
#> 2860 40H1 2000 13.18982 7.19185509 2000_40H1 niche_data NA
#> 2861 40H1 2001 13.18982 7.37156771 2001_40H1 niche_data NA
#> 2862 40H1 2002 13.18982 7.59728574 2002_40H1 niche_data NA
#> 2863 40H1 2003 13.18982 7.12506958 2003_40H1 niche_data NA
#> 2864 40H1 2004 13.18982 7.65046426 2004_40H1 niche_data NA
#> 2865 40H1 2005 13.18982 7.27986552 2005_40H1 niche_data NA
#> 2866 40H1 2006 13.18982 7.14262932 2006_40H1 niche_data NA
#> 2867 40H1 2007 13.18982 7.15018764 2007_40H1 niche_data NA
#> 2868 40H1 2008 13.18982 7.48599405 2008_40H1 niche_data NA
#> 2869 40H1 2009 13.18982 7.46993707 2009_40H1 niche_data NA
#> 2870 40H1 2010 13.18982 7.60644904 2010_40H1 niche_data NA
#> 2871 40H1 2011 13.18982 7.31931898 2011_40H1 niche_data NA
#> 2872 40H1 2012 13.18982 7.38152185 2012_40H1 niche_data NA
#> 2873 40H1 2013 13.18982 7.44941703 2013_40H1 niche_data NA
#> 2874 40H1 2014 13.18982 7.51954068 2014_40H1 niche_data NA
#> 2875 40H1 2015 13.18982 7.33573727 2015_40H1 niche_data NA
#> 2876 40H1 2016 13.18982 7.74778505 2016_40H1 niche_data NA
#> 2877 40H1 2017 13.18982 7.47806416 2017_40H1 niche_data NA
#> 2878 40H1 2018 13.18982 7.18133051 2018_40H1 niche_data NA
#> 2879 40H1 2019 13.18982 7.38656169 2019_40H1 niche_data NA
#> 2880 41G4 1993 29.22442 7.66056021 1993_41G4 niche_data NA
#> 2881 41G4 1994 29.22442 7.45234631 1994_41G4 niche_data NA
#> 2882 41G4 1995 29.22442 6.94411951 1995_41G4 niche_data NA
#> 2883 41G4 1996 29.22442 7.21988025 1996_41G4 niche_data NA
#> 2884 41G4 1997 29.22442 7.62431948 1997_41G4 niche_data NA
#> 2885 41G4 1998 29.22442 7.70125693 1998_41G4 niche_data NA
#> 2886 41G4 1999 29.22442 7.69384207 1999_41G4 niche_data NA
#> 2887 41G4 2000 29.22442 6.91988984 2000_41G4 niche_data NA
#> 2888 41G4 2001 29.22442 7.29063214 2001_41G4 niche_data NA
#> 2889 41G4 2002 29.22442 7.58115988 2002_41G4 niche_data NA
#> 2890 41G4 2003 29.22442 7.55986832 2003_41G4 niche_data NA
#> 2891 41G4 2004 29.22442 7.77263941 2004_41G4 niche_data NA
#> 2892 41G4 2005 29.22442 7.53867894 2005_41G4 niche_data NA
#> 2893 41G4 2006 29.22442 7.10086731 2006_41G4 niche_data NA
#> 2894 41G4 2007 29.22442 7.23592604 2007_41G4 niche_data NA
#> 2895 41G4 2008 29.22442 7.20942611 2008_41G4 niche_data NA
#> 2896 41G4 2009 29.22442 7.83967088 2009_41G4 niche_data NA
#> 2897 41G4 2010 29.22442 7.57369406 2010_41G4 niche_data NA
#> 2898 41G4 2011 29.22442 7.70051496 2011_41G4 niche_data NA
#> 2899 41G4 2012 29.22442 7.41120771 2012_41G4 niche_data NA
#> 2900 41G4 2013 29.22442 7.08645872 2013_41G4 niche_data NA
#> 2901 41G4 2014 29.22442 7.31129774 2014_41G4 niche_data NA
#> 2902 41G4 2015 29.22442 7.27558323 2015_41G4 niche_data NA
#> 2903 41G4 2016 29.22442 7.21916562 2016_41G4 niche_data NA
#> 2904 41G4 2017 29.22442 7.23886359 2017_41G4 niche_data NA
#> 2905 41G4 2018 29.22442 7.75843358 2018_41G4 niche_data NA
#> 2906 41G4 2019 29.22442 7.71037704 2019_41G4 niche_data NA
#> 2907 41G5 1993 34.39299 7.76013927 1993_41G5 niche_data NA
#> 2908 41G5 1994 34.39299 7.58057826 1994_41G5 niche_data NA
#> 2909 41G5 1995 34.39299 7.19083529 1995_41G5 niche_data NA
#> 2910 41G5 1996 34.39299 7.50230180 1996_41G5 niche_data NA
#> 2911 41G5 1997 34.39299 7.89047207 1997_41G5 niche_data NA
#> 2912 41G5 1998 34.39299 7.75781746 1998_41G5 niche_data NA
#> 2913 41G5 1999 34.39299 7.89821806 1999_41G5 niche_data NA
#> 2914 41G5 2000 34.39299 7.35182425 2000_41G5 niche_data NA
#> 2915 41G5 2001 34.39299 7.53732334 2001_41G5 niche_data NA
#> 2916 41G5 2002 34.39299 7.75094648 2002_41G5 niche_data NA
#> 2917 41G5 2003 34.39299 7.74432544 2003_41G5 niche_data NA
#> 2918 41G5 2004 34.39299 7.66013497 2004_41G5 niche_data NA
#> 2919 41G5 2005 34.39299 8.05143985 2005_41G5 niche_data NA
#> 2920 41G5 2006 34.39299 7.42255899 2006_41G5 niche_data NA
#> 2921 41G5 2007 34.39299 7.46013589 2007_41G5 niche_data NA
#> 2922 41G5 2008 34.39299 7.30024381 2008_41G5 niche_data NA
#> 2923 41G5 2009 34.39299 7.82440918 2009_41G5 niche_data NA
#> 2924 41G5 2010 34.39299 7.57815225 2010_41G5 niche_data NA
#> 2925 41G5 2011 34.39299 7.07066604 2011_41G5 niche_data NA
#> 2926 41G5 2012 34.39299 7.17843880 2012_41G5 niche_data NA
#> 2927 41G5 2013 34.39299 7.07767122 2013_41G5 niche_data NA
#> 2928 41G5 2014 34.39299 7.12154771 2014_41G5 niche_data NA
#> 2929 41G5 2015 34.39299 7.14734944 2015_41G5 niche_data NA
#> 2930 41G5 2016 34.39299 7.13405120 2016_41G5 niche_data NA
#> 2931 41G5 2017 34.39299 7.13528823 2017_41G5 niche_data NA
#> 2932 41G5 2018 34.39299 7.74903899 2018_41G5 niche_data NA
#> 2933 41G5 2019 34.39299 7.41379322 2019_41G5 niche_data NA
#> 2934 41G6 1993 32.27464 7.65958786 1993_41G6 niche_data NA
#> 2935 41G6 1994 32.27464 7.35507010 1994_41G6 niche_data NA
#> 2936 41G6 1995 32.27464 7.28047796 1995_41G6 niche_data NA
#> 2937 41G6 1996 32.27464 7.22587455 1996_41G6 niche_data NA
#> 2938 41G6 1997 32.27464 7.75848151 1997_41G6 niche_data NA
#> 2939 41G6 1998 32.27464 7.26127803 1998_41G6 niche_data NA
#> 2940 41G6 1999 32.27464 7.61267123 1999_41G6 niche_data NA
#> 2941 41G6 2000 32.27464 7.30705022 2000_41G6 niche_data NA
#> 2942 41G6 2001 32.27464 7.60571260 2001_41G6 niche_data NA
#> 2943 41G6 2002 32.27464 7.70979607 2002_41G6 niche_data NA
#> 2944 41G6 2004 32.27464 7.30891457 2004_41G6 niche_data NA
#> 2945 41G6 2010 32.27464 7.10978431 2010_41G6 niche_data NA
#> 2946 41G6 2011 32.23659 6.25036252 2011_41G6 niche_data NA
#> 2947 41G6 2012 32.27464 7.15040203 2012_41G6 niche_data NA
#> 2948 41G6 2013 32.19853 7.19427230 2013_41G6 niche_data NA
#> 2949 41G6 2015 32.27464 6.89059324 2015_41G6 niche_data NA
#> 2950 41G6 2017 32.27464 6.77858231 2017_41G6 niche_data NA
#> 2951 41G6 2018 32.27464 7.67497859 2018_41G6 niche_data NA
#> 2952 41G6 2019 32.27464 7.12551594 2019_41G6 niche_data NA
#> 2953 41G7 2010 38.00000 6.23922185 2010_41G7 niche_data NA
#> 2954 41G8 1993 39.15152 7.80294946 1993_41G8 niche_data NA
#> 2955 41G8 2001 37.56447 7.55019106 2001_41G8 niche_data NA
#> 2956 41G8 2002 39.02779 7.82399539 2002_41G8 niche_data NA
#> 2957 41G8 2018 37.28142 7.29830756 2018_41G8 niche_data NA
#> 2958 41G9 1993 59.00000 7.41879402 1993_41G9 niche_data NA
#> 2959 41G9 1994 59.00000 7.05301770 1994_41G9 niche_data NA
#> 2960 41G9 1995 59.00000 6.99429527 1995_41G9 niche_data NA
#> 2961 41G9 1996 59.00000 7.12437150 1996_41G9 niche_data NA
#> 2962 41G9 1998 59.00000 5.59297151 1998_41G9 niche_data NA
#> 2963 41G9 1999 59.00000 7.07381712 1999_41G9 niche_data NA
#> 2964 41G9 2001 59.00000 6.49376846 2001_41G9 niche_data NA
#> 2965 41G9 2002 59.00000 7.00862717 2002_41G9 niche_data NA
#> 2966 41G9 2004 59.00000 7.09680449 2004_41G9 niche_data NA
#> 2967 41G9 2007 59.00000 6.62970039 2007_41G9 niche_data NA
#> 2968 41G9 2009 59.00000 6.37828084 2009_41G9 niche_data NA
#> 2969 41G9 2013 59.00000 6.95524638 2013_41G9 niche_data NA
#> 2970 41G9 2019 59.00000 6.22208488 2019_41G9 niche_data NA
#> 2971 41H0 1993 28.00000 7.54085392 1993_41H0 niche_data NA
#> 2972 41H0 1994 28.00000 7.54677024 1994_41H0 niche_data NA
#> 2973 41H0 1995 28.00000 7.50044064 1995_41H0 niche_data NA
#> 2974 41H0 1996 28.00000 7.78747938 1996_41H0 niche_data NA
#> 2975 41H0 1997 28.00000 7.52516227 1997_41H0 niche_data NA
#> 2976 41H0 1998 28.00000 7.56652545 1998_41H0 niche_data NA
#> 2977 41H0 2001 28.00000 7.74480162 2001_41H0 niche_data NA
#> 2978 41H0 2002 28.00000 7.91847817 2002_41H0 niche_data NA
#> 2979 41H0 2003 28.00000 7.27802471 2003_41H0 niche_data NA
#> 2980 41H0 2004 28.00000 7.51547739 2004_41H0 niche_data NA
#> 2981 41H0 2005 28.00000 7.54784349 2005_41H0 niche_data NA
#> 2982 41H0 2006 28.00000 7.49917698 2006_41H0 niche_data NA
#> 2983 41H0 2007 28.00000 7.40985642 2007_41H0 niche_data NA
#> 2984 41H0 2009 28.00000 7.51484142 2009_41H0 niche_data NA
#> 2985 41H0 2013 28.00000 7.75241491 2013_41H0 niche_data NA
#> 2986 41H0 2014 28.00000 7.60765683 2014_41H0 niche_data NA
#> 2987 41H0 2015 28.00000 7.38608069 2015_41H0 niche_data NA
#> 2988 41H0 2019 28.00000 7.34015259 2019_41H0 niche_data NA
#> 2989 41H1 1993 11.11676 7.67226039 1993_41H1 niche_data NA
#> 2990 41H1 1994 11.11676 7.40334892 1994_41H1 niche_data NA
#> 2991 41H1 1995 11.11676 7.34137785 1995_41H1 niche_data NA
#> 2992 41H1 1996 11.11676 7.89110937 1996_41H1 niche_data NA
#> 2993 41H1 1997 11.11676 7.29637583 1997_41H1 niche_data NA
#> 2994 41H1 1998 11.11676 7.56440082 1998_41H1 niche_data NA
#> 2995 41H1 1999 11.11676 7.55951397 1999_41H1 niche_data NA
#> 2996 41H1 2000 11.11676 7.22091540 2000_41H1 niche_data NA
#> 2997 41H1 2001 11.11676 7.49940079 2001_41H1 niche_data NA
#> 2998 41H1 2002 11.11676 7.84510655 2002_41H1 niche_data NA
#> 2999 41H1 2003 11.11676 7.19102042 2003_41H1 niche_data NA
#> 3000 41H1 2004 11.11676 7.63260665 2004_41H1 niche_data NA
#> 3001 41H1 2005 11.11676 7.25788373 2005_41H1 niche_data NA
#> 3002 41H1 2006 11.11676 7.23212700 2006_41H1 niche_data NA
#> 3003 41H1 2007 11.11676 7.12188686 2007_41H1 niche_data NA
#> 3004 41H1 2008 11.11676 7.51304572 2008_41H1 niche_data NA
#> 3005 41H1 2009 11.11676 7.43739221 2009_41H1 niche_data NA
#> 3006 41H1 2010 11.11676 7.69574607 2010_41H1 niche_data NA
#> 3007 41H1 2011 11.11676 7.43306156 2011_41H1 niche_data NA
#> 3008 41H1 2012 11.11676 7.49419732 2012_41H1 niche_data NA
#> 3009 41H1 2013 11.11676 7.55566754 2013_41H1 niche_data NA
#> 3010 41H1 2014 11.11676 7.60086473 2014_41H1 niche_data NA
#> 3011 41H1 2015 11.11676 7.45818187 2015_41H1 niche_data NA
#> 3012 41H1 2016 11.11676 7.89068095 2016_41H1 niche_data NA
#> 3013 41H1 2017 11.11676 7.63635804 2017_41H1 niche_data NA
#> 3014 41H1 2018 11.11676 7.36072421 2018_41H1 niche_data NA
#> 3015 41H1 2019 11.11676 7.49874547 2019_41H1 niche_data NA
#> 3016 42G6 1993 29.09908 7.73389630 1993_42G6 niche_data NA
#> 3017 42G6 1994 29.09908 7.42195232 1994_42G6 niche_data NA
#> 3018 42G6 1995 29.09908 7.50306251 1995_42G6 niche_data NA
#> 3019 42G6 1996 29.09908 7.01549591 1996_42G6 niche_data NA
#> 3020 42G6 1997 29.09908 7.60542062 1997_42G6 niche_data NA
#> 3021 42G6 1998 29.09908 7.61993613 1998_42G6 niche_data NA
#> 3022 42G6 1999 29.09908 7.58934345 1999_42G6 niche_data NA
#> 3023 42G6 2000 29.09908 7.35193473 2000_42G6 niche_data NA
#> 3024 42G6 2004 29.09908 7.91552127 2004_42G6 niche_data NA
#> 3025 42G6 2005 29.09908 7.87826817 2005_42G6 niche_data NA
#> 3026 42G6 2012 29.09908 7.52284180 2012_42G6 niche_data NA
#> 3027 42G6 2013 27.03456 7.41232609 2013_42G6 niche_data NA
#> 3028 42G6 2015 29.09908 7.09332054 2015_42G6 niche_data NA
#> 3029 42G7 1993 73.23766 5.55988441 1993_42G7 niche_data NA
#> 3030 42G7 1994 72.97348 3.18922340 1994_42G7 niche_data NA
#> 3031 42G7 1995 73.23766 4.66670488 1995_42G7 niche_data NA
#> 3032 42G7 1996 61.65511 4.36988028 1996_42G7 niche_data NA
#> 3033 42G7 2003 58.23156 3.49780907 2003_42G7 niche_data NA
#> 3034 42G7 2004 65.51952 3.38180996 2004_42G7 niche_data NA
#> 3035 42G7 2005 72.00000 4.41963278 2005_42G7 niche_data NA
#> 3036 42G7 2006 56.11858 3.47964370 2006_42G7 niche_data NA
#> 3037 42G7 2008 61.65511 3.61602957 2008_42G7 niche_data NA
#> 3038 42G7 2010 70.37270 3.25399007 2010_42G7 niche_data NA
#> 3039 42G7 2013 56.46892 3.96087132 2013_42G7 niche_data NA
#> 3040 42G7 2015 59.72240 4.01575051 2015_42G7 niche_data NA
#> 3041 42G7 2017 57.28530 2.74269908 2017_42G7 niche_data NA
#> 3042 42G8 1993 30.05963 7.86838936 1993_42G8 niche_data NA
#> 3043 42G8 1994 30.05963 8.06999630 1994_42G8 niche_data NA
#> 3044 42G8 1995 30.05963 7.85548749 1995_42G8 niche_data NA
#> 3045 42G8 1996 30.05963 7.66208459 1996_42G8 niche_data NA
#> 3046 42G8 1997 30.04837 8.03324711 1997_42G8 niche_data NA
#> 3047 42G8 1998 30.04837 7.81544158 1998_42G8 niche_data NA
#> 3048 42G8 1999 29.27410 7.85263337 1999_42G8 niche_data NA
#> 3049 42G8 2000 29.94156 7.55160666 2000_42G8 niche_data NA
#> 3050 42G8 2001 29.94156 8.05420446 2001_42G8 niche_data NA
#> 3051 42G8 2002 30.00000 7.85462486 2002_42G8 niche_data NA
#> 3052 42G8 2003 30.00000 7.87900199 2003_42G8 niche_data NA
#> 3053 42G8 2004 30.00000 7.82425001 2004_42G8 niche_data NA
#> 3054 42G8 2005 30.00000 7.88728273 2005_42G8 niche_data NA
#> 3055 42G8 2006 29.11179 7.52481547 2006_42G8 niche_data NA
#> 3056 42G8 2007 29.83714 7.83737891 2007_42G8 niche_data NA
#> 3057 42G8 2008 29.83714 7.73775751 2008_42G8 niche_data NA
#> 3058 42G8 2009 29.11179 7.63860022 2009_42G8 niche_data NA
#> 3059 42G8 2010 29.83714 7.88721276 2010_42G8 niche_data NA
#> 3060 42G8 2011 29.43642 7.66552653 2011_42G8 niche_data NA
#> 3061 42G8 2012 29.83714 7.47061622 2012_42G8 niche_data NA
#> 3062 42G8 2013 29.88312 7.65452095 2013_42G8 niche_data NA
#> 3063 42G8 2014 29.94156 7.47670101 2014_42G8 niche_data NA
#> 3064 42G8 2015 28.88192 7.47202489 2015_42G8 niche_data NA
#> 3065 42G8 2016 29.83714 7.60266949 2016_42G8 niche_data NA
#> 3066 42G8 2017 28.70168 7.29741089 2017_42G8 niche_data NA
#> 3067 42G8 2018 30.00000 7.70575883 2018_42G8 niche_data NA
#> 3068 42G8 2019 30.00000 7.61437571 2019_42G8 niche_data NA
#> 3069 42H0 1993 43.82713 7.55470842 1993_42H0 niche_data NA
#> 3070 42H0 1994 43.82713 7.57355889 1994_42H0 niche_data NA
#> 3071 42H0 1995 43.82713 7.47107857 1995_42H0 niche_data NA
#> 3072 42H0 1996 43.82713 7.91582332 1996_42H0 niche_data NA
#> 3073 42H0 1998 43.82713 7.60109615 1998_42H0 niche_data NA
#> 3074 42H1 1993 18.30025 7.98388131 1993_42H1 niche_data NA
#> 3075 42H1 1994 18.30025 7.73413010 1994_42H1 niche_data NA
#> 3076 42H1 1995 18.30025 7.53345711 1995_42H1 niche_data NA
#> 3077 42H1 1996 18.30025 7.94023218 1996_42H1 niche_data NA
#> 3078 42H1 1997 18.30025 7.45455444 1997_42H1 niche_data NA
#> 3079 42H1 1998 18.30025 7.78536054 1998_42H1 niche_data NA
#> 3080 42H1 1999 18.30025 7.65605818 1999_42H1 niche_data NA
#> 3081 42H1 2000 18.30025 7.38661038 2000_42H1 niche_data NA
#> 3082 42H1 2001 18.30025 7.68775677 2001_42H1 niche_data NA
#> 3083 42H1 2002 18.30025 8.01096340 2002_42H1 niche_data NA
#> 3084 42H1 2003 18.30025 7.37061505 2003_42H1 niche_data NA
#> 3085 42H1 2004 18.30025 7.66457465 2004_42H1 niche_data NA
#> 3086 42H1 2005 18.30025 7.50144000 2005_42H1 niche_data NA
#> 3087 42H1 2006 18.30025 7.52218130 2006_42H1 niche_data NA
#> 3088 42H1 2007 18.30025 7.60482170 2007_42H1 niche_data NA
#> 3089 42H1 2008 18.30025 7.71537788 2008_42H1 niche_data NA
#> 3090 42H1 2009 18.30025 7.72670913 2009_42H1 niche_data NA
#> 3091 42H1 2010 18.30025 7.91550677 2010_42H1 niche_data NA
#> 3092 42H1 2011 18.30025 7.61017497 2011_42H1 niche_data NA
#> 3093 42H1 2012 18.30025 7.66729048 2012_42H1 niche_data NA
#> 3094 42H1 2013 18.30025 7.70589821 2013_42H1 niche_data NA
#> 3095 42H1 2014 18.30025 7.65363411 2014_42H1 niche_data NA
#> 3096 42H1 2015 18.30025 7.54550955 2015_42H1 niche_data NA
#> 3097 42H1 2016 18.30025 7.99614969 2016_42H1 niche_data NA
#> 3098 42H1 2017 18.30025 7.79599645 2017_42H1 niche_data NA
#> 3099 42H1 2018 18.30025 7.55210058 2018_42H1 niche_data NA
#> 3100 42H1 2019 18.30025 7.56149286 2019_42H1 niche_data NA
#> 3101 43G6 1993 30.91015 7.59425001 1993_43G6 niche_data NA
#> 3102 43G6 1994 30.91015 6.98145891 1994_43G6 niche_data NA
#> 3103 43G6 1995 30.91015 7.42949538 1995_43G6 niche_data NA
#> 3104 43G6 1996 30.91015 7.09143358 1996_43G6 niche_data NA
#> 3105 43G6 1998 30.91015 6.90988165 1998_43G6 niche_data NA
#> 3106 43G6 2004 30.91015 7.77762803 2004_43G6 niche_data NA
#> 3107 43G6 2013 30.91015 6.91941191 2013_43G6 niche_data NA
#> 3108 43G6 2014 30.91015 6.70009889 2014_43G6 niche_data NA
#> 3109 43G6 2016 30.91015 7.66555512 2016_43G6 niche_data NA
#> 3110 43G6 2017 30.91015 6.90064678 2017_43G6 niche_data NA
#> 3111 43G6 2018 30.91015 7.40607598 2018_43G6 niche_data NA
#> 3112 43G7 1999 56.05859 5.60292947 1999_43G7 niche_data NA
#> 3113 43G7 2003 52.91734 5.38141735 2003_43G7 niche_data NA
#> 3114 43G7 2004 55.36378 6.63740292 2004_43G7 niche_data NA
#> 3115 43G7 2006 54.28073 4.69075291 2006_43G7 niche_data NA
#> 3116 43G7 2012 55.69919 6.27523528 2012_43G7 niche_data NA
#> 3117 43G7 2013 55.25524 5.22319414 2013_43G7 niche_data NA
#> 3118 43G7 2015 52.67770 5.20981991 2015_43G7 niche_data NA
#> 3119 43G8 2001 27.22653 8.03733456 2001_43G8 niche_data NA
#> 3120 43G8 2002 27.22653 7.84996093 2002_43G8 niche_data NA
#> 3121 43G8 2005 27.22653 7.90168527 2005_43G8 niche_data NA
#> 3122 43G9 1996 66.36603 5.76541124 1996_43G9 niche_data NA
#> 3123 43G9 1998 65.81358 4.31112624 1998_43G9 niche_data NA
#> 3124 43G9 2001 65.81358 4.99153745 2001_43G9 niche_data NA
#> 3125 43G9 2002 66.08981 6.66818526 2002_43G9 niche_data NA
#> 3126 43G9 2004 66.36603 3.44164284 2004_43G9 niche_data NA
#> 3127 43G9 2006 61.90562 4.17563407 2006_43G9 niche_data NA
#> 3128 43G9 2012 65.81358 5.05298159 2012_43G9 niche_data NA
#> 3129 43G9 2014 66.08981 4.49921251 2014_43G9 niche_data NA
#> 3130 43G9 2015 61.39362 4.26680143 2015_43G9 niche_data NA
#> 3131 43G9 2016 62.41762 3.96178732 2016_43G9 niche_data NA
#> 3132 43G9 2019 65.81358 4.75566628 2019_43G9 niche_data NA
#> 3133 43H0 1993 52.76306 7.67080781 1993_43H0 niche_data NA
#> 3134 43H0 1994 52.76306 7.48433354 1994_43H0 niche_data NA
#> 3135 43H0 1995 52.76306 7.43212180 1995_43H0 niche_data NA
#> 3136 43H0 1996 52.76306 7.87986123 1996_43H0 niche_data NA
#> 3137 43H0 2005 52.76306 7.52695544 2005_43H0 niche_data NA
#> 3138 43H0 2006 52.76306 7.29674344 2006_43H0 niche_data NA
#> 3139 43H0 2009 52.76306 7.13575589 2009_43H0 niche_data NA
#> 3140 43H0 2015 52.76306 7.40447357 2015_43H0 niche_data NA
#> 3141 43H1 1993 23.00000 7.70678900 1993_43H1 niche_data NA
#> 3142 43H1 1994 23.00000 7.50578169 1994_43H1 niche_data NA
#> 3143 43H1 1995 23.00000 7.44672206 1995_43H1 niche_data NA
#> 3144 43H1 1996 23.00000 7.92396006 1996_43H1 niche_data NA
#> 3145 43H1 2000 23.00000 7.24706226 2000_43H1 niche_data NA
#> 3146 43H1 2004 23.00000 7.56359968 2004_43H1 niche_data NA
#> 3147 43H1 2007 23.00000 7.47169877 2007_43H1 niche_data NA
#> 3148 43H1 2008 23.00000 7.63645153 2008_43H1 niche_data NA
#> 3149 43H1 2016 23.00000 7.90658215 2016_43H1 niche_data NA
#> 3150 44G6 1993 50.69443 7.68461121 1993_44G6 niche_data NA
#> 3151 44G6 1994 50.69443 7.15367464 1994_44G6 niche_data NA
#> 3152 44G6 1995 50.69443 7.82556761 1995_44G6 niche_data NA
#> 3153 44G6 1996 50.69443 7.68534888 1996_44G6 niche_data NA
#> 3154 44G6 1997 50.69443 7.92742019 1997_44G6 niche_data NA
#> 3155 44G6 1998 50.69443 7.66305466 1998_44G6 niche_data NA
#> 3156 44G6 1999 50.69443 7.54103293 1999_44G6 niche_data NA
#> 3157 44G6 2000 50.69443 7.62919222 2000_44G6 niche_data NA
#> 3158 44G6 2001 50.69443 7.55328329 2001_44G6 niche_data NA
#> 3159 44G6 2002 50.69443 7.86135257 2002_44G6 niche_data NA
#> 3160 44G6 2003 50.69443 7.70986296 2003_44G6 niche_data NA
#> 3161 44G6 2004 50.69443 7.94261685 2004_44G6 niche_data NA
#> 3162 44G6 2005 50.69443 7.79756388 2005_44G6 niche_data NA
#> 3163 44G6 2006 50.69443 6.90588567 2006_44G6 niche_data NA
#> 3164 44G6 2007 50.69443 7.54885020 2007_44G6 niche_data NA
#> 3165 44G6 2008 50.69443 7.04478808 2008_44G6 niche_data NA
#> 3166 44G6 2009 50.69443 8.13603276 2009_44G6 niche_data NA
#> 3167 44G6 2010 50.69443 7.28397441 2010_44G6 niche_data NA
#> 3168 44G6 2011 50.69443 5.91376869 2011_44G6 niche_data NA
#> 3169 44G6 2012 50.69443 7.64334032 2012_44G6 niche_data NA
#> 3170 44G6 2013 50.69443 6.54638622 2013_44G6 niche_data NA
#> 3171 44G6 2014 50.69443 6.45347120 2014_44G6 niche_data NA
#> 3172 44G6 2015 50.69443 5.36760772 2015_44G6 niche_data NA
#> 3173 44G6 2016 50.69443 7.57570168 2016_44G6 niche_data NA
#> 3174 44G6 2017 50.69443 6.76077353 2017_44G6 niche_data NA
#> 3175 44G6 2018 50.69443 7.65040617 2018_44G6 niche_data NA
#> 3176 44G6 2019 50.69443 7.46095143 2019_44G6 niche_data NA
#> 3177 44G7 1993 83.47453 5.73880798 1993_44G7 niche_data NA
#> 3178 44G7 1994 83.19037 3.55642896 1994_44G7 niche_data NA
#> 3179 44G7 1995 82.63989 5.25981280 1995_44G7 niche_data NA
#> 3180 44G7 1996 81.77901 3.96215107 1996_44G7 niche_data NA
#> 3181 44G7 1997 81.03609 4.80467277 1997_44G7 niche_data NA
#> 3182 44G7 1998 81.03609 4.64660676 1998_44G7 niche_data NA
#> 3183 44G7 1999 81.46861 4.29224988 1999_44G7 niche_data NA
#> 3184 44G7 2000 81.46861 6.06195513 2000_44G7 niche_data NA
#> 3185 44G7 2001 80.60356 4.30420686 2001_44G7 niche_data NA
#> 3186 44G7 2002 81.03609 5.25261056 2002_44G7 niche_data NA
#> 3187 44G7 2003 77.01854 4.71610538 2003_44G7 niche_data NA
#> 3188 44G7 2004 81.03609 4.65323357 2004_44G7 niche_data NA
#> 3189 44G7 2005 81.77901 6.15668864 2005_44G7 niche_data NA
#> 3190 44G7 2006 72.53629 4.41542022 2006_44G7 niche_data NA
#> 3191 44G7 2007 80.60356 4.50387367 2007_44G7 niche_data NA
#> 3192 44G7 2008 81.03609 4.20145782 2008_44G7 niche_data NA
#> 3193 44G7 2009 81.03609 5.57961805 2009_44G7 niche_data NA
#> 3194 44G7 2010 80.43781 4.41419233 2010_44G7 niche_data NA
#> 3195 44G7 2011 80.43781 3.54937987 2011_44G7 niche_data NA
#> 3196 44G7 2012 81.03609 4.95336450 2012_44G7 niche_data NA
#> 3197 44G7 2013 80.60356 3.53202611 2013_44G7 niche_data NA
#> 3198 44G7 2014 81.03609 4.08567790 2014_44G7 niche_data NA
#> 3199 44G7 2015 72.08430 3.27012011 2015_44G7 niche_data NA
#> 3200 44G7 2016 80.60356 4.11482901 2016_44G7 niche_data NA
#> 3201 44G7 2017 80.43781 3.49388255 2017_44G7 niche_data NA
#> 3202 44G7 2018 81.03609 4.48265880 2018_44G7 niche_data NA
#> 3203 44G7 2019 82.63989 4.20275734 2019_44G7 niche_data NA
#> 3204 44G8 1993 27.80362 7.72392169 1993_44G8 niche_data NA
#> 3205 44G8 1995 27.80362 7.62250144 1995_44G8 niche_data NA
#> 3206 44G8 1996 27.80362 7.60706400 1996_44G8 niche_data NA
#> 3207 44G8 1997 27.54758 7.70949797 1997_44G8 niche_data NA
#> 3208 44G8 1998 27.54758 7.77212630 1998_44G8 niche_data NA
#> 3209 44G8 1999 27.54758 7.56527501 1999_44G8 niche_data NA
#> 3210 44G8 2000 27.54758 7.63720782 2000_44G8 niche_data NA
#> 3211 44G8 2001 27.00000 8.01716905 2001_44G8 niche_data NA
#> 3212 44G8 2002 27.54758 7.74000147 2002_44G8 niche_data NA
#> 3213 44G8 2003 26.97803 7.56442483 2003_44G8 niche_data NA
#> 3214 44G8 2004 27.00000 7.83413962 2004_44G8 niche_data NA
#> 3215 44G8 2005 27.67238 7.65312705 2005_44G8 niche_data NA
#> 3216 44G8 2006 26.97803 7.21979521 2006_44G8 niche_data NA
#> 3217 44G8 2007 26.97803 7.24528532 2007_44G8 niche_data NA
#> 3218 44G8 2008 27.00000 7.50773159 2008_44G8 niche_data NA
#> 3219 44G8 2009 26.97803 7.32158972 2009_44G8 niche_data NA
#> 3220 44G8 2010 27.00000 7.96554871 2010_44G8 niche_data NA
#> 3221 44G8 2011 26.97803 7.86155967 2011_44G8 niche_data NA
#> 3222 44G8 2012 27.00000 7.46068614 2012_44G8 niche_data NA
#> 3223 44G8 2013 27.00000 7.62878594 2013_44G8 niche_data NA
#> 3224 44G8 2014 27.00000 7.70787402 2014_44G8 niche_data NA
#> 3225 44G8 2015 27.00000 7.49783467 2015_44G8 niche_data NA
#> 3226 44G8 2016 27.00000 7.65435180 2016_44G8 niche_data NA
#> 3227 44G8 2017 26.56589 7.60768734 2017_44G8 niche_data NA
#> 3228 44G8 2018 27.00000 7.51447770 2018_44G8 niche_data NA
#> 3229 44G8 2019 26.97803 7.44794445 2019_44G8 niche_data NA
#> 3230 44G9 1996 46.52287 6.84888951 1996_44G9 niche_data NA
#> 3231 44G9 2002 46.52287 7.92068257 2002_44G9 niche_data NA
#> 3232 44G9 2003 46.17992 7.17043670 2003_44G9 niche_data NA
#> 3233 44G9 2007 46.02985 5.89811967 2007_44G9 niche_data NA
#> 3234 44G9 2008 45.98064 6.87075240 2008_44G9 niche_data NA
#> 3235 44H0 1993 56.07331 7.43251699 1993_44H0 niche_data NA
#> 3236 44H0 1994 56.07331 6.95833773 1994_44H0 niche_data NA
#> 3237 44H0 1995 56.07331 7.07065140 1995_44H0 niche_data NA
#> 3238 44H0 1996 56.07331 7.54987979 1996_44H0 niche_data NA
#> 3239 44H0 1997 56.07331 7.17430696 1997_44H0 niche_data NA
#> 3240 44H0 1998 56.07331 6.62525222 1998_44H0 niche_data NA
#> 3241 44H0 1999 56.07331 6.75264530 1999_44H0 niche_data NA
#> 3242 44H0 2000 56.07331 6.93494539 2000_44H0 niche_data NA
#> 3243 44H0 2001 56.07331 6.56063747 2001_44H0 niche_data NA
#> 3244 44H0 2002 56.07331 6.81166376 2002_44H0 niche_data NA
#> 3245 44H0 2003 56.07331 6.06552475 2003_44H0 niche_data NA
#> 3246 44H0 2004 56.07331 7.04603485 2004_44H0 niche_data NA
#> 3247 44H0 2005 56.07331 6.91532677 2005_44H0 niche_data NA
#> 3248 44H0 2006 56.07331 6.42195804 2006_44H0 niche_data NA
#> 3249 44H0 2007 56.07331 6.62464985 2007_44H0 niche_data NA
#> 3250 44H0 2008 56.07331 7.29359075 2008_44H0 niche_data NA
#> 3251 44H0 2011 56.07331 6.71013207 2011_44H0 niche_data NA
#> 3252 44H0 2012 56.07331 7.35145920 2012_44H0 niche_data NA
#> 3253 44H0 2013 56.07331 7.11978550 2013_44H0 niche_data NA
#> 3254 44H0 2014 56.07331 6.65335013 2014_44H0 niche_data NA
#> 3255 44H0 2016 56.07331 6.88567812 2016_44H0 niche_data NA
#> 3256 44H0 2017 56.07331 7.55688774 2017_44H0 niche_data NA
#> 3257 44H0 2018 56.07331 5.63771488 2018_44H0 niche_data NA
#> 3258 44H1 1993 30.23161 7.66812010 1993_44H1 niche_data NA
#> 3259 44H1 1994 30.23161 7.48463470 1994_44H1 niche_data NA
#> 3260 44H1 1995 30.23161 7.64794919 1995_44H1 niche_data NA
#> 3261 44H1 1996 30.23161 7.87837719 1996_44H1 niche_data NA
#> 3262 44H1 1999 30.23161 7.83634315 1999_44H1 niche_data NA
#> 3263 44H1 2001 30.23161 7.62386028 2001_44H1 niche_data NA
#> 3264 44H1 2002 30.23161 8.11188027 2002_44H1 niche_data NA
#> 3265 44H1 2003 30.23161 7.45708704 2003_44H1 niche_data NA
#> 3266 44H1 2004 30.23161 7.60755997 2004_44H1 niche_data NA
#> 3267 44H1 2006 30.23161 7.61031516 2006_44H1 niche_data NA
all_dat <- all_dat %>%
drop_na(n) %>%
mutate(small_sample = ifelse(n < 30, "Y", "N"))# %>% filter(small_sample == "N")
#> drop_na: removed 1,535 rows (47%), 1,732 rows remaining
#> mutate: new variable 'small_sample' (character) with 2 unique values and 0% NA
# Depth
ggplot(all_dat %>% filter(year == 1999)) +
geom_bar(aes(ices_rect, median_depth, fill = type), position="dodge", stat="identity") +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~small_sample, ncol = 1) +
NULL
#> filter: removed 1,690 rows (98%), 42 rows remaining
# Oxygen
ggplot(all_dat %>% filter(year == 1999)) +
geom_bar(aes(ices_rect, median_oxy, fill = type), position="dodge", stat="identity") +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~small_sample, ncol = 1) +
NULL
#> filter: removed 1,690 rows (98%), 42 rows remaining
# The fine scale variables are matched to haul. However, the ices_rect level variables
# and sub division averages should not be averages of the hauls in those areas, but rather
# average prediction on those levels. To achieve that, I will join in those averages from the pred_gridknitr::knit_exit()